# select, filter, %>%
library(dplyr)
## `parse_quosure`
library(rlang)
data <- read.csv('data/payments_ppdb_app_category_code_aggregated_LT01.csv')
## Only first cluster
x <- data %>% filter(age >= 50 & 0 < approval_real_price_sum_by_by_approval_type_LT01)
target <- x %>% select(approval_real_price_sum_by_by_approval_type_LT01)
category_code <- x %>% select(matches("^category_code_LT01_\\d{1,2}_count$")) %>% cbind(target)
for(i in seq(2, 6)) {
cat("i: ", i, '\n')
combinations <- combn(16, i)
for(j in seq(ncol(combinations))) {
# cat("j: ", j, '\n')
comb.vector <- combinations[,j]
comb.representation <- paste(paste0('category_code_LT01_', comb.vector, '_count'), collapse = '+')
tmp <- category_code %>% mutate(new_category_count_col = UQ(parse_quosure(comb.representation)))
single.formula_str <- paste('approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col')
single.summary.object <- ((tmp + 1) %>% log %>% lm(single.formula_str %>% as.formula, .) %>% summary)
if(single.summary.object$adj.r.squared > 0.7) {
cat('########################################\n')
cat(comb.representation, '\n')
cat(paste(single.formula_str, single.summary.object$adj.r.squared, '\n'))
print(single.summary.object)
cat('########################################\n')
}
multiple.formula_str <- paste('approval_real_price_sum_by_by_approval_type_LT01 ~ ', comb.representation)
multiple.summary.object <- ((tmp + 1) %>% log %>% lm(multiple.formula_str %>% as.formula, .) %>% summary)
if(multiple.summary.object$adj.r.squared > 0.6) {
cat('########################################\n')
cat(comb.representation, '\n')
cat(paste(multiple.formula_str, multiple.summary.object$adj.r.squared, '\n'))
print(multiple.summary.object)
cat('########################################\n')
}
}
}
## i: 2
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count 0.609930794153804
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0518 -0.7622 -0.0038 0.9382 4.0296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01233 0.08835 113.33 <2e-16 ***
## category_code_LT01_4_count 1.10057 0.06484 16.97 <2e-16 ***
## category_code_LT01_5_count 0.94441 0.06261 15.09 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 495 degrees of freedom
## Multiple R-squared: 0.6115, Adjusted R-squared: 0.6099
## F-statistic: 389.6 on 2 and 495 DF, p-value: < 2.2e-16
##
## ########################################
## i: 3
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count 0.618577686520257
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0258 -0.7994 0.0041 0.8836 3.4490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94420 0.08822 112.719 < 2e-16 ***
## category_code_LT01_1_count 0.44824 0.08263 5.425 9.11e-08 ***
## category_code_LT01_2_count 0.84631 0.07027 12.044 < 2e-16 ***
## category_code_LT01_5_count 1.00516 0.06091 16.504 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 494 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6186
## F-statistic: 269.7 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count 0.618512370116477
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7681 -0.0092 0.9314 3.8490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97858 0.08791 113.514 < 2e-16 ***
## category_code_LT01_1_count 0.30914 0.08874 3.484 0.000539 ***
## category_code_LT01_4_count 0.94359 0.07837 12.039 < 2e-16 ***
## category_code_LT01_5_count 0.95758 0.06203 15.437 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 494 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6185
## F-statistic: 269.6 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count 0.611841986963208
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0309 -0.7641 0.0094 0.8695 3.8264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97454 0.08857 112.61 < 2e-16 ***
## category_code_LT01_2_count 0.83284 0.07588 10.98 < 2e-16 ***
## category_code_LT01_3_count 0.49328 0.10937 4.51 8.09e-06 ***
## category_code_LT01_5_count 0.96875 0.06205 15.61 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 494 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6118
## F-statistic: 262.1 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count 0.638158949842913
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9820 -0.7482 0.0436 0.8466 3.4639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95865 0.08552 116.452 < 2e-16 ***
## category_code_LT01_2_count 0.54869 0.08718 6.294 6.83e-10 ***
## category_code_LT01_4_count 0.68801 0.09053 7.599 1.51e-13 ***
## category_code_LT01_5_count 0.92115 0.06041 15.248 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 494 degrees of freedom
## Multiple R-squared: 0.6403, Adjusted R-squared: 0.6382
## F-statistic: 293.2 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count 0.605227057073404
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0422 -0.8156 -0.0034 0.9715 3.8209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98003 0.08933 111.719 < 2e-16 ***
## category_code_LT01_2_count 0.90695 0.07212 12.575 < 2e-16 ***
## category_code_LT01_5_count 0.97713 0.06262 15.605 < 2e-16 ***
## category_code_LT01_6_count 0.52736 0.15401 3.424 0.000668 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 494 degrees of freedom
## Multiple R-squared: 0.6076, Adjusted R-squared: 0.6052
## F-statistic: 255 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count 0.610575099009888
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0617 -0.7836 -0.0125 0.9359 3.8042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99675 0.08854 112.903 < 2e-16 ***
## category_code_LT01_2_count 0.90205 0.06889 13.094 < 2e-16 ***
## category_code_LT01_5_count 0.98114 0.06185 15.862 < 2e-16 ***
## category_code_LT01_7_count 0.65740 0.15214 4.321 1.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 494 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.6106
## F-statistic: 260.7 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count 0.611645789740146
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0698 -0.7786 -0.0018 0.8929 4.2741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00592 0.08843 113.152 < 2e-16 ***
## category_code_LT01_2_count 0.76145 0.08618 8.835 < 2e-16 ***
## category_code_LT01_5_count 0.97957 0.06178 15.855 < 2e-16 ***
## category_code_LT01_11_count 0.50568 0.11284 4.481 9.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 494 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6116
## F-statistic: 261.9 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count 0.618313867410239
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0187 -0.7812 0.0320 0.8611 3.4256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99247 0.08758 114.091 < 2e-16 ***
## category_code_LT01_3_count 0.38829 0.11269 3.446 0.000618 ***
## category_code_LT01_4_count 0.92900 0.08120 11.441 < 2e-16 ***
## category_code_LT01_5_count 0.92523 0.06218 14.880 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 494 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6183
## F-statistic: 269.4 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.618001558003724
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0125 -0.7797 -0.0061 0.9293 4.0517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99015 0.08767 113.946 < 2e-16 ***
## category_code_LT01_4_count 0.98153 0.07317 13.414 < 2e-16 ***
## category_code_LT01_5_count 0.91967 0.06239 14.742 < 2e-16 ***
## category_code_LT01_6_count 0.50928 0.15045 3.385 0.000768 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 494 degrees of freedom
## Multiple R-squared: 0.6203, Adjusted R-squared: 0.618
## F-statistic: 269 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.618158892682412
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.7553 0.0053 0.8625 4.0313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01061 0.08741 114.521 < 2e-16 ***
## category_code_LT01_4_count 0.98522 0.07250 13.589 < 2e-16 ***
## category_code_LT01_5_count 0.93141 0.06206 15.008 < 2e-16 ***
## category_code_LT01_7_count 0.52660 0.15417 3.416 0.000689 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 494 degrees of freedom
## Multiple R-squared: 0.6205, Adjusted R-squared: 0.6182
## F-statistic: 269.2 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.609308982558757
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0562 -0.7728 -0.0115 0.9483 4.0283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01362 0.08846 113.196 <2e-16 ***
## category_code_LT01_4_count 1.10149 0.06492 16.966 <2e-16 ***
## category_code_LT01_5_count 0.94883 0.06339 14.969 <2e-16 ***
## category_code_LT01_8_count -0.12833 0.27861 -0.461 0.645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 494 degrees of freedom
## Multiple R-squared: 0.6117, Adjusted R-squared: 0.6093
## F-statistic: 259.4 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.613667666108038
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7934 0.0204 0.9317 4.0380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00383 0.08799 113.687 <2e-16 ***
## category_code_LT01_4_count 1.05806 0.06691 15.814 <2e-16 ***
## category_code_LT01_5_count 0.93175 0.06253 14.901 <2e-16 ***
## category_code_LT01_9_count 0.54369 0.22599 2.406 0.0165 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 494 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6137
## F-statistic: 264.2 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.611077212420303
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0110 -0.7808 0.0228 0.9257 3.8738
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97287 0.09174 108.712 <2e-16 ***
## category_code_LT01_4_count 1.07835 0.06628 16.270 <2e-16 ***
## category_code_LT01_5_count 0.94239 0.06253 15.071 <2e-16 ***
## category_code_LT01_10_count 0.17767 0.11330 1.568 0.117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 494 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6111
## F-statistic: 261.3 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.623135276287295
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0407 -0.7314 0.0308 0.9156 3.7053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01171 0.08684 115.289 < 2e-16 ***
## category_code_LT01_4_count 0.84822 0.08680 9.773 < 2e-16 ***
## category_code_LT01_5_count 0.92925 0.06164 15.075 < 2e-16 ***
## category_code_LT01_11_count 0.46866 0.10942 4.283 2.22e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 494 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6231
## F-statistic: 274.9 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.609920988143405
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0456 -0.8001 0.0020 0.9316 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01052 0.08837 113.282 <2e-16 ***
## category_code_LT01_4_count 1.08226 0.06741 16.055 <2e-16 ***
## category_code_LT01_5_count 0.93811 0.06293 14.908 <2e-16 ***
## category_code_LT01_12_count 0.20620 0.20750 0.994 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 494 degrees of freedom
## Multiple R-squared: 0.6123, Adjusted R-squared: 0.6099
## F-statistic: 260 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.609401773495551
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0508 -0.7625 -0.0053 0.9252 4.0299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01201 0.08841 113.246 <2e-16 ***
## category_code_LT01_4_count 1.09324 0.06613 16.532 <2e-16 ***
## category_code_LT01_5_count 0.94343 0.06267 15.053 <2e-16 ***
## category_code_LT01_13_count 0.14189 0.24715 0.574 0.566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 494 degrees of freedom
## Multiple R-squared: 0.6118, Adjusted R-squared: 0.6094
## F-statistic: 259.5 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.609293242553254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0515 -0.7870 -0.0033 0.9248 4.0279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01394 0.08850 113.157 <2e-16 ***
## category_code_LT01_4_count 1.09317 0.06706 16.302 <2e-16 ***
## category_code_LT01_5_count 0.94155 0.06300 14.946 <2e-16 ***
## category_code_LT01_14_count 0.14590 0.33274 0.438 0.661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 494 degrees of freedom
## Multiple R-squared: 0.6117, Adjusted R-squared: 0.6093
## F-statistic: 259.4 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.609291533003517
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0520 -0.7689 -0.0043 0.9382 4.0296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01227 0.08842 113.235 <2e-16 ***
## category_code_LT01_4_count 1.09501 0.06614 16.557 <2e-16 ***
## category_code_LT01_5_count 0.94476 0.06266 15.077 <2e-16 ***
## category_code_LT01_15_count 0.33173 0.76083 0.436 0.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 494 degrees of freedom
## Multiple R-squared: 0.6116, Adjusted R-squared: 0.6093
## F-statistic: 259.3 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.610052333152422
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0503 -0.7649 -0.0036 0.9386 4.0296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01226 0.08833 113.345 <2e-16 ***
## category_code_LT01_4_count 1.09532 0.06501 16.847 <2e-16 ***
## category_code_LT01_5_count 0.94235 0.06263 15.047 <2e-16 ***
## category_code_LT01_16_count 1.26824 1.18044 1.074 0.283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 494 degrees of freedom
## Multiple R-squared: 0.6124, Adjusted R-squared: 0.6101
## F-statistic: 260.2 on 3 and 494 DF, p-value: < 2.2e-16
##
## ########################################
## i: 4
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count 0.62658896441048
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9945 -0.7743 0.0483 0.8309 3.4598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93335 0.08735 113.721 < 2e-16 ***
## category_code_LT01_1_count 0.38093 0.08411 4.529 7.45e-06 ***
## category_code_LT01_2_count 0.72470 0.07816 9.272 < 2e-16 ***
## category_code_LT01_3_count 0.37584 0.11036 3.406 0.000714 ***
## category_code_LT01_5_count 0.97562 0.06088 16.024 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 493 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6266
## F-statistic: 209.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count 0.643645047532432
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9654 -0.7660 0.0798 0.8576 3.4757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93372 0.08529 116.469 < 2e-16 ***
## category_code_LT01_1_count 0.25310 0.08628 2.933 0.00351 **
## category_code_LT01_2_count 0.52100 0.08703 5.987 4.13e-09 ***
## category_code_LT01_4_count 0.58031 0.09706 5.979 4.31e-09 ***
## category_code_LT01_5_count 0.93311 0.06009 15.528 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 493 degrees of freedom
## Multiple R-squared: 0.6465, Adjusted R-squared: 0.6436
## F-statistic: 225.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count 0.623677233945147
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9970 -0.8123 0.0407 0.9482 3.4607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93243 0.08773 113.213 < 2e-16 ***
## category_code_LT01_1_count 0.41623 0.08288 5.022 7.16e-07 ***
## category_code_LT01_2_count 0.76236 0.07608 10.021 < 2e-16 ***
## category_code_LT01_5_count 0.98064 0.06114 16.039 < 2e-16 ***
## category_code_LT01_6_count 0.42120 0.15185 2.774 0.00575 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 493 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6237
## F-statistic: 206.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count 0.627252540516555
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7802 0.0411 0.9198 3.4452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94795 0.08722 114.057 < 2e-16 ***
## category_code_LT01_1_count 0.39842 0.08289 4.807 2.04e-06 ***
## category_code_LT01_2_count 0.76397 0.07327 10.427 < 2e-16 ***
## category_code_LT01_5_count 0.98359 0.06052 16.253 < 2e-16 ***
## category_code_LT01_7_count 0.53397 0.15105 3.535 0.000446 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 493 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6273
## F-statistic: 210.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count 0.617975830985733
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0302 -0.7930 -0.0003 0.9041 3.4478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94539 0.08833 112.597 < 2e-16 ***
## category_code_LT01_1_count 0.44979 0.08276 5.435 8.64e-08 ***
## category_code_LT01_2_count 0.84624 0.07032 12.033 < 2e-16 ***
## category_code_LT01_5_count 1.00976 0.06173 16.357 < 2e-16 ***
## category_code_LT01_8_count -0.12979 0.27564 -0.471 0.638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.618
## F-statistic: 202 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count 0.620359367725214
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0149 -0.8033 0.0279 0.9105 3.4526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94052 0.08804 112.912 < 2e-16 ***
## category_code_LT01_1_count 0.43707 0.08267 5.287 1.87e-07 ***
## category_code_LT01_2_count 0.81740 0.07188 11.372 < 2e-16 ***
## category_code_LT01_5_count 0.99475 0.06103 16.299 < 2e-16 ***
## category_code_LT01_9_count 0.41175 0.22603 1.822 0.0691 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 493 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6204
## F-statistic: 204 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count 0.619379102047929
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9893 -0.7914 0.0220 0.8954 3.4837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90942 0.09143 108.383 < 2e-16 ***
## category_code_LT01_1_count 0.44719 0.08255 5.417 9.47e-08 ***
## category_code_LT01_2_count 0.82621 0.07159 11.541 < 2e-16 ***
## category_code_LT01_5_count 1.00268 0.06087 16.473 < 2e-16 ***
## category_code_LT01_10_count 0.16059 0.11243 1.428 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 493 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6194
## F-statistic: 203.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count 0.625485913986026
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7622 0.0453 0.8829 3.4352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95792 0.08753 113.772 < 2e-16 ***
## category_code_LT01_1_count 0.37372 0.08517 4.388 1.40e-05 ***
## category_code_LT01_2_count 0.68314 0.08649 7.898 1.86e-14 ***
## category_code_LT01_5_count 0.98498 0.06068 16.231 < 2e-16 ***
## category_code_LT01_11_count 0.36651 0.11526 3.180 0.00157 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 493 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6255
## F-statistic: 208.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count 0.617804076230663
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0258 -0.7994 0.0040 0.8833 3.4490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.944194 0.088312 112.603 < 2e-16 ***
## category_code_LT01_1_count 0.448349 0.083545 5.367 1.24e-07 ***
## category_code_LT01_2_count 0.846446 0.071841 11.782 < 2e-16 ***
## category_code_LT01_5_count 1.005220 0.061345 16.386 < 2e-16 ***
## category_code_LT01_12_count -0.001925 0.208947 -0.009 0.993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6178
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count 0.617819149959011
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0257 -0.7980 0.0046 0.8856 3.4488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94434 0.08831 112.601 < 2e-16 ***
## category_code_LT01_1_count 0.44633 0.08383 5.324 1.54e-07 ***
## category_code_LT01_2_count 0.84555 0.07055 11.985 < 2e-16 ***
## category_code_LT01_5_count 1.00479 0.06102 16.466 < 2e-16 ***
## category_code_LT01_13_count 0.03449 0.24683 0.140 0.889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6178
## F-statistic: 201.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count 0.617921715282845
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7941 0.0063 0.8888 3.4471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94606 0.08843 112.479 < 2e-16 ***
## category_code_LT01_1_count 0.44364 0.08354 5.311 1.66e-07 ***
## category_code_LT01_2_count 0.84275 0.07092 11.883 < 2e-16 ***
## category_code_LT01_5_count 1.00210 0.06146 16.305 < 2e-16 ***
## category_code_LT01_14_count 0.12844 0.32958 0.390 0.697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6179
## F-statistic: 201.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_15_count 0.617907159980514
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0251 -0.8012 0.0129 0.8800 3.4497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94348 0.08832 112.584 < 2e-16 ***
## category_code_LT01_1_count 0.45412 0.08426 5.390 1.1e-07 ***
## category_code_LT01_2_count 0.84824 0.07053 12.027 < 2e-16 ***
## category_code_LT01_5_count 1.00511 0.06096 16.488 < 2e-16 ***
## category_code_LT01_15_count -0.27920 0.76532 -0.365 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6179
## F-statistic: 201.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_16_count 0.61787637712049
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0258 -0.7974 0.0065 0.8898 3.4487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94444 0.08831 112.614 < 2e-16 ***
## category_code_LT01_1_count 0.44919 0.08277 5.427 8.99e-08 ***
## category_code_LT01_2_count 0.84332 0.07101 11.876 < 2e-16 ***
## category_code_LT01_5_count 1.00483 0.06097 16.480 < 2e-16 ***
## category_code_LT01_16_count 0.35966 1.17707 0.306 0.76
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6179
## F-statistic: 201.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count 0.624666988359471
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0011 -0.7585 0.0318 0.8570 3.4375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96522 0.08731 114.141 < 2e-16 ***
## category_code_LT01_1_count 0.27196 0.08888 3.060 0.00234 **
## category_code_LT01_3_count 0.34041 0.11284 3.017 0.00269 **
## category_code_LT01_4_count 0.81205 0.08913 9.111 < 2e-16 ***
## category_code_LT01_5_count 0.93918 0.06183 15.190 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 493 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6247
## F-statistic: 207.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.625102553305449
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9932 -0.7669 0.0515 0.9425 3.8838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96101 0.08733 114.066 < 2e-16 ***
## category_code_LT01_1_count 0.28429 0.08834 3.218 0.00137 **
## category_code_LT01_4_count 0.84735 0.08362 10.133 < 2e-16 ***
## category_code_LT01_5_count 0.93389 0.06196 15.072 < 2e-16 ***
## category_code_LT01_6_count 0.46572 0.14966 3.112 0.00197 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 493 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6251
## F-statistic: 208.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.625150179410957
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.7695 0.0167 0.8686 3.8662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97992 0.08714 114.528 < 2e-16 ***
## category_code_LT01_1_count 0.28246 0.08838 3.196 0.00148 **
## category_code_LT01_4_count 0.85218 0.08302 10.264 < 2e-16 ***
## category_code_LT01_5_count 0.94461 0.06163 15.328 < 2e-16 ***
## category_code_LT01_7_count 0.47917 0.15347 3.122 0.00190 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 493 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6252
## F-statistic: 208.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.617997295042587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0325 -0.7532 -0.0058 0.9254 3.8465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97999 0.08800 113.409 < 2e-16 ***
## category_code_LT01_1_count 0.31079 0.08885 3.498 0.000511 ***
## category_code_LT01_4_count 0.94389 0.07843 12.035 < 2e-16 ***
## category_code_LT01_5_count 0.96313 0.06281 15.334 < 2e-16 ***
## category_code_LT01_8_count -0.15928 0.27564 -0.578 0.563628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.618
## F-statistic: 202 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.621548448062337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0123 -0.7891 0.0311 0.9200 3.8636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97205 0.08760 113.830 < 2e-16 ***
## category_code_LT01_1_count 0.29748 0.08855 3.360 0.000841 ***
## category_code_LT01_4_count 0.91048 0.07946 11.458 < 2e-16 ***
## category_code_LT01_5_count 0.94546 0.06202 15.244 < 2e-16 ***
## category_code_LT01_9_count 0.49916 0.22406 2.228 0.026345 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 493 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6215
## F-statistic: 205.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.619753369105945
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9855 -0.7759 0.0341 0.9115 3.6896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93823 0.09124 108.918 < 2e-16 ***
## category_code_LT01_1_count 0.31038 0.08860 3.503 0.000502 ***
## category_code_LT01_4_count 0.92031 0.07956 11.567 < 2e-16 ***
## category_code_LT01_5_count 0.95557 0.06194 15.427 < 2e-16 ***
## category_code_LT01_10_count 0.18107 0.11203 1.616 0.106681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 493 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6198
## F-statistic: 203.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.628107258451039
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7644 0.0251 0.9054 3.6018
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98492 0.08681 115.020 < 2e-16 ***
## category_code_LT01_1_count 0.24613 0.08925 2.758 0.006038 **
## category_code_LT01_4_count 0.75455 0.09267 8.142 3.2e-15 ***
## category_code_LT01_5_count 0.94161 0.06140 15.337 < 2e-16 ***
## category_code_LT01_11_count 0.41051 0.11073 3.707 0.000233 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 493 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6281
## F-statistic: 210.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.617977971708148
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0243 -0.7678 0.0029 0.9351 3.8538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97827 0.08797 113.429 < 2e-16 ***
## category_code_LT01_1_count 0.30266 0.08957 3.379 0.000785 ***
## category_code_LT01_4_count 0.93666 0.07942 11.794 < 2e-16 ***
## category_code_LT01_5_count 0.95378 0.06245 15.273 < 2e-16 ***
## category_code_LT01_12_count 0.11512 0.20710 0.556 0.578572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.618
## F-statistic: 202 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.617745380398712
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0271 -0.7682 -0.0063 0.9178 3.8498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97865 0.08800 113.396 < 2e-16 ***
## category_code_LT01_1_count 0.30797 0.08972 3.433 0.000648 ***
## category_code_LT01_4_count 0.94299 0.07871 11.980 < 2e-16 ***
## category_code_LT01_5_count 0.95737 0.06213 15.408 < 2e-16 ***
## category_code_LT01_13_count 0.02316 0.24693 0.094 0.925323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6177
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.617744963194073
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7686 -0.0047 0.9315 3.8492
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97900 0.08812 113.248 < 2e-16 ***
## category_code_LT01_1_count 0.30832 0.08929 3.453 0.000602 ***
## category_code_LT01_4_count 0.94248 0.07940 11.871 < 2e-16 ***
## category_code_LT01_5_count 0.95695 0.06247 15.318 < 2e-16 ***
## category_code_LT01_14_count 0.03006 0.33083 0.091 0.927637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6177
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.617763567980324
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7678 -0.0060 0.9317 3.8473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97829 0.08801 113.380 < 2e-16 ***
## category_code_LT01_1_count 0.31202 0.09026 3.457 0.000594 ***
## category_code_LT01_4_count 0.94443 0.07859 12.017 < 2e-16 ***
## category_code_LT01_5_count 0.95755 0.06209 15.422 < 2e-16 ***
## category_code_LT01_15_count -0.13733 0.76467 -0.180 0.857548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6178
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.618731033324284
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7679 0.0019 0.9334 3.8483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97836 0.08788 113.544 < 2e-16 ***
## category_code_LT01_1_count 0.31048 0.08873 3.499 0.000509 ***
## category_code_LT01_4_count 0.93744 0.07854 11.936 < 2e-16 ***
## category_code_LT01_5_count 0.95548 0.06204 15.401 < 2e-16 ***
## category_code_LT01_16_count 1.32240 1.16733 1.133 0.257834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 493 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6187
## F-statistic: 202.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count 0.640748714538151
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9672 -0.7337 0.0660 0.8725 3.4828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95072 0.08529 116.667 < 2e-16 ***
## category_code_LT01_2_count 0.50415 0.08933 5.644 2.81e-08 ***
## category_code_LT01_3_count 0.24014 0.11244 2.136 0.0332 *
## category_code_LT01_4_count 0.61540 0.09640 6.384 4.00e-10 ***
## category_code_LT01_5_count 0.91118 0.06038 15.092 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 493 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6407
## F-statistic: 222.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count 0.617549420444578
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7635 0.0221 0.8876 3.8408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96019 0.08806 113.109 < 2e-16 ***
## category_code_LT01_2_count 0.74787 0.08084 9.251 < 2e-16 ***
## category_code_LT01_3_count 0.45060 0.10956 4.113 4.58e-05 ***
## category_code_LT01_5_count 0.94617 0.06209 15.239 < 2e-16 ***
## category_code_LT01_6_count 0.44264 0.15298 2.893 0.00398 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6175
## F-statistic: 201.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count 0.622738476965924
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0165 -0.7578 -0.0179 0.8373 3.8267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97427 0.08732 114.227 < 2e-16 ***
## category_code_LT01_2_count 0.73835 0.07862 9.392 < 2e-16 ***
## category_code_LT01_3_count 0.44634 0.10849 4.114 4.55e-05 ***
## category_code_LT01_5_count 0.94835 0.06140 15.446 < 2e-16 ***
## category_code_LT01_7_count 0.58876 0.15067 3.907 0.000106 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 493 degrees of freedom
## Multiple R-squared: 0.6258, Adjusted R-squared: 0.6227
## F-statistic: 206.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count 0.611175458619494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7652 0.0074 0.8621 3.8253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97563 0.08869 112.475 < 2e-16 ***
## category_code_LT01_2_count 0.83279 0.07594 10.966 < 2e-16 ***
## category_code_LT01_3_count 0.49459 0.10951 4.516 7.88e-06 ***
## category_code_LT01_5_count 0.97251 0.06285 15.474 < 2e-16 ***
## category_code_LT01_8_count -0.10880 0.27799 -0.391 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 493 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6112
## F-statistic: 196.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count 0.612877045290232
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0231 -0.7631 0.0227 0.8829 3.8294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97155 0.08848 112.704 < 2e-16 ***
## category_code_LT01_2_count 0.81458 0.07672 10.617 < 2e-16 ***
## category_code_LT01_3_count 0.46744 0.11053 4.229 2.8e-05 ***
## category_code_LT01_5_count 0.96188 0.06214 15.480 < 2e-16 ***
## category_code_LT01_9_count 0.35092 0.23035 1.523 0.128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 493 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6129
## F-statistic: 197.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count 0.611391518619168
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7522 0.0109 0.8401 3.8422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95880 0.09184 108.441 < 2e-16 ***
## category_code_LT01_2_count 0.82842 0.07622 10.868 < 2e-16 ***
## category_code_LT01_3_count 0.48007 0.11128 4.314 1.94e-05 ***
## category_code_LT01_5_count 0.96863 0.06209 15.600 < 2e-16 ***
## category_code_LT01_10_count 0.07552 0.11552 0.654 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 493 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6114
## F-statistic: 196.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count 0.619641821156081
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0323 -0.7735 -0.0100 0.8269 4.0630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98499 0.08773 113.811 < 2e-16 ***
## category_code_LT01_2_count 0.67116 0.08939 7.508 2.83e-13 ***
## category_code_LT01_3_count 0.38218 0.11327 3.374 0.000799 ***
## category_code_LT01_5_count 0.95563 0.06155 15.525 < 2e-16 ***
## category_code_LT01_11_count 0.38978 0.11683 3.336 0.000913 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 493 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6196
## F-statistic: 203.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count 0.611171891459006
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.7639 -0.0059 0.8705 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97432 0.08865 112.513 < 2e-16 ***
## category_code_LT01_2_count 0.82671 0.07759 10.654 < 2e-16 ***
## category_code_LT01_3_count 0.48988 0.10982 4.461 1.01e-05 ***
## category_code_LT01_5_count 0.96636 0.06241 15.483 < 2e-16 ***
## category_code_LT01_12_count 0.08071 0.20934 0.386 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 493 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6112
## F-statistic: 196.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count 0.611578479258562
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0292 -0.7714 0.0069 0.8910 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97417 0.08860 112.571 < 2e-16 ***
## category_code_LT01_2_count 0.82547 0.07644 10.799 < 2e-16 ***
## category_code_LT01_3_count 0.48936 0.10951 4.469 9.77e-06 ***
## category_code_LT01_5_count 0.96686 0.06212 15.565 < 2e-16 ***
## category_code_LT01_13_count 0.20039 0.24575 0.815 0.415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 493 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.6116
## F-statistic: 196.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count 0.61208413860337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0284 -0.7679 0.0159 0.8542 3.8226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97831 0.08861 112.615 < 2e-16 ***
## category_code_LT01_2_count 0.81693 0.07712 10.593 < 2e-16 ***
## category_code_LT01_3_count 0.49332 0.10933 4.512 8.03e-06 ***
## category_code_LT01_5_count 0.95971 0.06254 15.347 < 2e-16 ***
## category_code_LT01_14_count 0.37605 0.32876 1.144 0.253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 493 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6121
## F-statistic: 197.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count 0.611055446491905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7641 0.0094 0.8701 3.8264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97456 0.08866 112.497 < 2e-16 ***
## category_code_LT01_2_count 0.83267 0.07616 10.933 < 2e-16 ***
## category_code_LT01_3_count 0.49278 0.11060 4.455 1.04e-05 ***
## category_code_LT01_5_count 0.96879 0.06213 15.593 < 2e-16 ***
## category_code_LT01_15_count 0.02436 0.76568 0.032 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6111
## F-statistic: 196.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_16_count 0.611113055008319
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0305 -0.7636 0.0073 0.8659 3.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97409 0.08867 112.485 < 2e-16 ***
## category_code_LT01_2_count 0.83421 0.07612 10.960 < 2e-16 ***
## category_code_LT01_3_count 0.49575 0.10984 4.513 7.99e-06 ***
## category_code_LT01_5_count 0.96884 0.06211 15.598 < 2e-16 ***
## category_code_LT01_16_count -0.32399 1.19065 -0.272 0.786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6111
## F-statistic: 196.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.640487850103678
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9638 -0.7384 0.0859 0.8953 3.4875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94949 0.08536 116.561 < 2e-16 ***
## category_code_LT01_2_count 0.50519 0.08945 5.648 2.75e-08 ***
## category_code_LT01_4_count 0.64875 0.09225 7.032 6.82e-12 ***
## category_code_LT01_5_count 0.90804 0.06056 14.995 < 2e-16 ***
## category_code_LT01_6_count 0.30792 0.15025 2.049 0.041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 493 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6405
## F-statistic: 222.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.642535553510534
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7037 0.0603 0.8606 3.4713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96058 0.08500 117.181 < 2e-16 ***
## category_code_LT01_2_count 0.51558 0.08754 5.890 7.17e-09 ***
## category_code_LT01_4_count 0.62527 0.09304 6.721 5.01e-11 ***
## category_code_LT01_5_count 0.91268 0.06013 15.178 < 2e-16 ***
## category_code_LT01_7_count 0.40011 0.15071 2.655 0.00819 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 493 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6425
## F-statistic: 224.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.63757336766471
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7494 0.0489 0.8432 3.4581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95988 0.08563 116.313 < 2e-16 ***
## category_code_LT01_2_count 0.54851 0.08725 6.287 7.14e-10 ***
## category_code_LT01_4_count 0.68901 0.09063 7.602 1.48e-13 ***
## category_code_LT01_5_count 0.92531 0.06117 15.128 < 2e-16 ***
## category_code_LT01_8_count -0.12056 0.26835 -0.449 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 493 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6376
## F-statistic: 219.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.639340285031251
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9732 -0.7487 0.0572 0.8712 3.4757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95503 0.08541 116.560 < 2e-16 ***
## category_code_LT01_2_count 0.52866 0.08791 6.014 3.54e-09 ***
## category_code_LT01_4_count 0.67517 0.09073 7.441 4.47e-13 ***
## category_code_LT01_5_count 0.91369 0.06049 15.105 < 2e-16 ***
## category_code_LT01_9_count 0.35686 0.22055 1.618 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 493 degrees of freedom
## Multiple R-squared: 0.6422, Adjusted R-squared: 0.6393
## F-statistic: 221.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.638097524764369
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9590 -0.7673 0.0662 0.8524 3.3717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93616 0.08869 112.029 < 2e-16 ***
## category_code_LT01_2_count 0.53971 0.08769 6.155 1.56e-09 ***
## category_code_LT01_4_count 0.68161 0.09079 7.508 2.84e-13 ***
## category_code_LT01_5_count 0.92033 0.06042 15.231 < 2e-16 ***
## category_code_LT01_10_count 0.10522 0.10992 0.957 0.339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 493 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6381
## F-statistic: 220.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.640533600777387
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7534 0.0815 0.8560 3.4616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96586 0.08531 116.822 < 2e-16 ***
## category_code_LT01_2_count 0.47181 0.09453 4.991 8.35e-07 ***
## category_code_LT01_4_count 0.61656 0.09665 6.380 4.09e-10 ***
## category_code_LT01_5_count 0.91665 0.06025 15.213 < 2e-16 ***
## category_code_LT01_11_count 0.24007 0.11627 2.065 0.0395 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 493 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6405
## F-statistic: 222.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.637431961292688
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7482 0.0466 0.8467 3.4646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95860 0.08560 116.332 < 2e-16 ***
## category_code_LT01_2_count 0.54742 0.08824 6.204 1.17e-09 ***
## category_code_LT01_4_count 0.68722 0.09099 7.553 2.08e-13 ***
## category_code_LT01_5_count 0.92061 0.06073 15.158 < 2e-16 ***
## category_code_LT01_12_count 0.01969 0.20229 0.097 0.922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 493 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6374
## F-statistic: 219.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.637486628552587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7482 0.0436 0.8455 3.4644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95861 0.08560 116.343 < 2e-16 ***
## category_code_LT01_2_count 0.54746 0.08736 6.267 8.05e-10 ***
## category_code_LT01_4_count 0.68537 0.09108 7.525 2.52e-13 ***
## category_code_LT01_5_count 0.92073 0.06049 15.222 < 2e-16 ***
## category_code_LT01_13_count 0.06902 0.23838 0.290 0.772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 493 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6375
## F-statistic: 219.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.637435786244791
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9820 -0.7487 0.0463 0.8365 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95913 0.08570 116.215 < 2e-16 ***
## category_code_LT01_2_count 0.54813 0.08739 6.273 7.78e-10 ***
## category_code_LT01_4_count 0.68646 0.09152 7.500 2.99e-13 ***
## category_code_LT01_5_count 0.92042 0.06078 15.144 < 2e-16 ***
## category_code_LT01_14_count 0.03889 0.32099 0.121 0.904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 493 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6374
## F-statistic: 219.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.637425849080165
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9821 -0.7482 0.0412 0.8470 3.4639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95866 0.08561 116.332 < 2e-16 ***
## category_code_LT01_2_count 0.54849 0.08746 6.272 7.82e-10 ***
## category_code_LT01_4_count 0.68774 0.09097 7.560 1.98e-13 ***
## category_code_LT01_5_count 0.92119 0.06048 15.231 < 2e-16 ***
## category_code_LT01_15_count 0.02507 0.73455 0.034 0.973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 493 degrees of freedom
## Multiple R-squared: 0.6403, Adjusted R-squared: 0.6374
## F-statistic: 219.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.637524002009119
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9820 -0.7486 0.0407 0.8447 3.4641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95900 0.08560 116.346 < 2e-16 ***
## category_code_LT01_2_count 0.54485 0.08788 6.200 1.20e-09 ***
## category_code_LT01_4_count 0.68916 0.09067 7.601 1.49e-13 ***
## category_code_LT01_5_count 0.92063 0.06048 15.222 < 2e-16 ***
## category_code_LT01_16_count 0.42064 1.14628 0.367 0.714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 493 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6375
## F-statistic: 219.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.618292931084458
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0231 -0.7775 -0.0325 0.9758 3.8229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97807 0.08784 113.592 < 2e-16 ***
## category_code_LT01_2_count 0.79016 0.07610 10.383 < 2e-16 ***
## category_code_LT01_5_count 0.95244 0.06185 15.400 < 2e-16 ***
## category_code_LT01_6_count 0.50238 0.15156 3.315 0.000984 ***
## category_code_LT01_7_count 0.63794 0.15074 4.232 2.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 493 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6183
## F-statistic: 202.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.604559837844542
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0460 -0.8035 -0.0030 0.9701 3.8198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98114 0.08945 111.586 < 2e-16 ***
## category_code_LT01_2_count 0.90678 0.07219 12.562 < 2e-16 ***
## category_code_LT01_5_count 0.98103 0.06339 15.475 < 2e-16 ***
## category_code_LT01_6_count 0.53024 0.15430 3.436 0.000639 ***
## category_code_LT01_8_count -0.11445 0.28051 -0.408 0.683434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 493 degrees of freedom
## Multiple R-squared: 0.6077, Adjusted R-squared: 0.6046
## F-statistic: 191 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.607566075884537
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.8386 0.0063 0.9785 3.8259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97510 0.08910 111.953 < 2e-16 ***
## category_code_LT01_2_count 0.87402 0.07380 11.844 < 2e-16 ***
## category_code_LT01_5_count 0.96657 0.06266 15.427 < 2e-16 ***
## category_code_LT01_6_count 0.50949 0.15382 3.312 0.000993 ***
## category_code_LT01_9_count 0.45594 0.22957 1.986 0.047581 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 493 degrees of freedom
## Multiple R-squared: 0.6107, Adjusted R-squared: 0.6076
## F-statistic: 193.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.605090485727374
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0199 -0.8068 -0.0173 0.9680 3.8430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95792 0.09259 107.552 < 2e-16 ***
## category_code_LT01_2_count 0.89871 0.07270 12.362 < 2e-16 ***
## category_code_LT01_5_count 0.97684 0.06263 15.598 < 2e-16 ***
## category_code_LT01_6_count 0.50448 0.15607 3.232 0.00131 **
## category_code_LT01_10_count 0.10565 0.11603 0.911 0.36296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 493 degrees of freedom
## Multiple R-squared: 0.6083, Adjusted R-squared: 0.6051
## F-statistic: 191.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.616659580847878
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0384 -0.8269 0.0291 0.9351 3.8490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98953 0.08806 113.439 < 2e-16 ***
## category_code_LT01_2_count 0.69312 0.08920 7.770 4.59e-14 ***
## category_code_LT01_5_count 0.95791 0.06189 15.477 < 2e-16 ***
## category_code_LT01_6_count 0.42097 0.15412 2.732 0.00653 **
## category_code_LT01_11_count 0.45156 0.11384 3.966 8.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 493 degrees of freedom
## Multiple R-squared: 0.6197, Adjusted R-squared: 0.6167
## F-statistic: 200.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.604525115297386
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0406 -0.8243 -0.0268 0.9652 3.8211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97990 0.08941 111.618 < 2e-16 ***
## category_code_LT01_2_count 0.90149 0.07385 12.208 < 2e-16 ***
## category_code_LT01_5_count 0.97505 0.06295 15.489 < 2e-16 ***
## category_code_LT01_6_count 0.52113 0.15517 3.358 0.000844 ***
## category_code_LT01_12_count 0.07435 0.21183 0.351 0.725758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 493 degrees of freedom
## Multiple R-squared: 0.6077, Adjusted R-squared: 0.6045
## F-statistic: 190.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.605232901186465
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0396 -0.8179 -0.0255 0.9730 3.8216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97933 0.08933 111.709 < 2e-16 ***
## category_code_LT01_2_count 0.89589 0.07296 12.279 < 2e-16 ***
## category_code_LT01_5_count 0.97440 0.06267 15.547 < 2e-16 ***
## category_code_LT01_6_count 0.52733 0.15401 3.424 0.000668 ***
## category_code_LT01_13_count 0.24842 0.24751 1.004 0.316040
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 493 degrees of freedom
## Multiple R-squared: 0.6084, Adjusted R-squared: 0.6052
## F-statistic: 191.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.60594372558056
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0380 -0.8234 0.0011 0.9437 3.8169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98407 0.08930 111.806 < 2e-16 ***
## category_code_LT01_2_count 0.88416 0.07393 11.959 < 2e-16 ***
## category_code_LT01_5_count 0.96526 0.06315 15.285 < 2e-16 ***
## category_code_LT01_6_count 0.54230 0.15425 3.516 0.000479 ***
## category_code_LT01_14_count 0.45768 0.33217 1.378 0.168879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.414 on 493 degrees of freedom
## Multiple R-squared: 0.6091, Adjusted R-squared: 0.6059
## F-statistic: 192.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.604604720187118
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0425 -0.8158 -0.0325 0.9713 3.8208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98017 0.08940 111.632 < 2e-16 ***
## category_code_LT01_2_count 0.90233 0.07284 12.387 < 2e-16 ***
## category_code_LT01_5_count 0.97739 0.06267 15.596 < 2e-16 ***
## category_code_LT01_6_count 0.52318 0.15439 3.389 0.000758 ***
## category_code_LT01_15_count 0.36101 0.76541 0.472 0.637378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 493 degrees of freedom
## Multiple R-squared: 0.6078, Adjusted R-squared: 0.6046
## F-statistic: 191 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.604578516090604
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0419 -0.8257 -0.0219 0.9715 3.8206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98031 0.08941 111.628 < 2e-16 ***
## category_code_LT01_2_count 0.90164 0.07320 12.317 < 2e-16 ***
## category_code_LT01_5_count 0.97627 0.06270 15.571 < 2e-16 ***
## category_code_LT01_6_count 0.53394 0.15488 3.448 0.000614 ***
## category_code_LT01_16_count 0.52375 1.20227 0.436 0.663292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 493 degrees of freedom
## Multiple R-squared: 0.6078, Adjusted R-squared: 0.6046
## F-statistic: 191 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.609923880574989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0657 -0.7875 -0.0193 0.9348 3.8030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99798 0.08867 112.761 < 2e-16 ***
## category_code_LT01_2_count 0.90208 0.06895 13.084 < 2e-16 ***
## category_code_LT01_5_count 0.98518 0.06266 15.724 < 2e-16 ***
## category_code_LT01_7_count 0.65984 0.15238 4.330 1.81e-05 ***
## category_code_LT01_8_count -0.11661 0.27851 -0.419 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 493 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6099
## F-statistic: 195.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.612132220965896
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0508 -0.7856 0.0036 0.9537 3.8089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99201 0.08841 113.022 < 2e-16 ***
## category_code_LT01_2_count 0.87550 0.07045 12.427 < 2e-16 ***
## category_code_LT01_5_count 0.97226 0.06194 15.696 < 2e-16 ***
## category_code_LT01_7_count 0.62816 0.15278 4.112 4.6e-05 ***
## category_code_LT01_9_count 0.39596 0.22925 1.727 0.0848 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 493 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6121
## F-statistic: 197.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.610702142152402
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0341 -0.7942 0.0093 0.9285 3.8309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97007 0.09192 108.461 < 2e-16 ***
## category_code_LT01_2_count 0.88916 0.06991 12.718 < 2e-16 ***
## category_code_LT01_5_count 0.97984 0.06186 15.841 < 2e-16 ***
## category_code_LT01_7_count 0.64266 0.15273 4.208 3.06e-05 ***
## category_code_LT01_10_count 0.12302 0.11416 1.078 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.6107
## F-statistic: 195.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.618706795240544
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0562 -0.7628 0.0170 0.9215 3.7986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00232 0.08763 114.144 < 2e-16 ***
## category_code_LT01_2_count 0.72249 0.08627 8.375 5.77e-16 ***
## category_code_LT01_5_count 0.96515 0.06139 15.723 < 2e-16 ***
## category_code_LT01_7_count 0.50140 0.15740 3.186 0.001536 **
## category_code_LT01_11_count 0.39702 0.11689 3.396 0.000738 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 493 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6187
## F-statistic: 202.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.610130601305114
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0579 -0.7856 -0.0215 0.9180 3.8049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99609 0.08860 112.824 < 2e-16 ***
## category_code_LT01_2_count 0.88962 0.07145 12.451 < 2e-16 ***
## category_code_LT01_5_count 0.97668 0.06226 15.688 < 2e-16 ***
## category_code_LT01_7_count 0.65542 0.15226 4.305 2.02e-05 ***
## category_code_LT01_12_count 0.13811 0.20898 0.661 0.509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6133, Adjusted R-squared: 0.6101
## F-statistic: 195.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.609932472501276
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0609 -0.7711 -0.0095 0.9369 3.8045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99648 0.08862 112.805 < 2e-16 ***
## category_code_LT01_2_count 0.89900 0.06931 12.971 < 2e-16 ***
## category_code_LT01_5_count 0.98033 0.06193 15.829 < 2e-16 ***
## category_code_LT01_7_count 0.64845 0.15367 4.220 2.91e-05 ***
## category_code_LT01_13_count 0.10713 0.24831 0.431 0.666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 493 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6099
## F-statistic: 195.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.610202938443773
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0605 -0.7888 -0.0056 0.9158 3.8017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99921 0.08865 112.795 < 2e-16 ***
## category_code_LT01_2_count 0.89394 0.06982 12.803 < 2e-16 ***
## category_code_LT01_5_count 0.97580 0.06232 15.658 < 2e-16 ***
## category_code_LT01_7_count 0.64668 0.15293 4.229 2.8e-05 ***
## category_code_LT01_14_count 0.24067 0.33110 0.727 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6133, Adjusted R-squared: 0.6102
## F-statistic: 195.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.610196305878174
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0616 -0.7697 -0.0170 0.9162 3.8042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99671 0.08859 112.848 < 2e-16 ***
## category_code_LT01_2_count 0.89335 0.06997 12.767 < 2e-16 ***
## category_code_LT01_5_count 0.98110 0.06188 15.854 < 2e-16 ***
## category_code_LT01_7_count 0.65864 0.15223 4.327 1.83e-05 ***
## category_code_LT01_15_count 0.54714 0.75878 0.721 0.471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6133, Adjusted R-squared: 0.6102
## F-statistic: 195.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.609815330883235
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0618 -0.7781 -0.0142 0.9359 3.8040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99698 0.08864 112.786 < 2e-16 ***
## category_code_LT01_2_count 0.90025 0.06957 12.939 < 2e-16 ***
## category_code_LT01_5_count 0.98090 0.06193 15.840 < 2e-16 ***
## category_code_LT01_7_count 0.65805 0.15233 4.320 1.89e-05 ***
## category_code_LT01_16_count 0.23200 1.18886 0.195 0.845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 493 degrees of freedom
## Multiple R-squared: 0.613, Adjusted R-squared: 0.6098
## F-statistic: 195.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.610893225467014
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0719 -0.7746 0.0013 0.8944 4.2727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00654 0.08856 112.987 < 2e-16 ***
## category_code_LT01_2_count 0.76182 0.08628 8.829 < 2e-16 ***
## category_code_LT01_5_count 0.98167 0.06264 15.673 < 2e-16 ***
## category_code_LT01_8_count -0.05868 0.27798 -0.211 0.833
## category_code_LT01_11_count 0.50545 0.11295 4.475 9.5e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6109
## F-statistic: 196.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.613536496170723
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0576 -0.7901 0.0188 0.9067 4.3325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00054 0.08826 113.306 < 2e-16 ***
## category_code_LT01_2_count 0.73597 0.08707 8.452 3.24e-16 ***
## category_code_LT01_5_count 0.96978 0.06186 15.677 < 2e-16 ***
## category_code_LT01_9_count 0.42173 0.22815 1.848 0.0651 .
## category_code_LT01_11_count 0.48906 0.11292 4.331 1.80e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 493 degrees of freedom
## Multiple R-squared: 0.6166, Adjusted R-squared: 0.6135
## F-statistic: 198.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.612023895437916
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0384 -0.7653 0.0175 0.8923 4.3328
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97575 0.09180 108.674 < 2e-16 ***
## category_code_LT01_2_count 0.74777 0.08687 8.608 < 2e-16 ***
## category_code_LT01_5_count 0.97786 0.06177 15.831 < 2e-16 ***
## category_code_LT01_10_count 0.13836 0.11368 1.217 0.224
## category_code_LT01_11_count 0.49809 0.11295 4.410 1.27e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 493 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.612
## F-statistic: 197 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.610979625540094
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0718 -0.7695 -0.0065 0.8909 4.2699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00647 0.08852 113.047 < 2e-16 ***
## category_code_LT01_2_count 0.76322 0.08637 8.836 < 2e-16 ***
## category_code_LT01_5_count 0.98171 0.06207 15.815 < 2e-16 ***
## category_code_LT01_11_count 0.51720 0.11669 4.432 1.15e-05 ***
## category_code_LT01_12_count -0.08464 0.21564 -0.393 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.611
## F-statistic: 196.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.611211844091931
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0682 -0.7762 0.0054 0.8961 4.2836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00538 0.08848 113.078 < 2e-16 ***
## category_code_LT01_2_count 0.75718 0.08647 8.757 < 2e-16 ***
## category_code_LT01_5_count 0.97809 0.06186 15.812 < 2e-16 ***
## category_code_LT01_11_count 0.49985 0.11323 4.414 1.25e-05 ***
## category_code_LT01_13_count 0.16501 0.24636 0.670 0.503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 493 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6112
## F-statistic: 196.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.611482692473015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0680 -0.7595 0.0059 0.8894 4.2908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00880 0.08851 113.086 < 2e-16 ***
## category_code_LT01_2_count 0.75205 0.08685 8.660 < 2e-16 ***
## category_code_LT01_5_count 0.97285 0.06226 15.627 < 2e-16 ***
## category_code_LT01_11_count 0.50000 0.11304 4.423 1.2e-05 ***
## category_code_LT01_14_count 0.29339 0.32954 0.890 0.374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 493 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6115
## F-statistic: 196.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.610955251297787
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0699 -0.7789 -0.0021 0.8904 4.2796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00587 0.08851 113.051 < 2e-16 ***
## category_code_LT01_2_count 0.75884 0.08658 8.765 < 2e-16 ***
## category_code_LT01_5_count 0.97974 0.06184 15.843 < 2e-16 ***
## category_code_LT01_11_count 0.50281 0.11323 4.441 1.11e-05 ***
## category_code_LT01_15_count 0.26671 0.75996 0.351 0.726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.611
## F-statistic: 196.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.610884361611228
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0699 -0.7777 -0.0016 0.8931 4.2776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00614 0.08852 113.033 < 2e-16 ***
## category_code_LT01_2_count 0.75968 0.08681 8.751 < 2e-16 ***
## category_code_LT01_5_count 0.97936 0.06185 15.833 < 2e-16 ***
## category_code_LT01_11_count 0.50605 0.11297 4.480 9.3e-06 ***
## category_code_LT01_16_count 0.21674 1.18715 0.183 0.855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6109
## F-statistic: 196.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.624500262149966
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9870 -0.7535 0.0057 0.9143 3.4654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97475 0.08707 114.563 < 2e-16 ***
## category_code_LT01_3_count 0.34788 0.11257 3.090 0.00211 **
## category_code_LT01_4_count 0.84070 0.08567 9.813 < 2e-16 ***
## category_code_LT01_5_count 0.90516 0.06203 14.592 < 2e-16 ***
## category_code_LT01_6_count 0.45415 0.15023 3.023 0.00263 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 493 degrees of freedom
## Multiple R-squared: 0.6275, Adjusted R-squared: 0.6245
## F-statistic: 207.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.625613193953221
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0102 -0.7634 0.0442 0.8601 3.4386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99188 0.08674 115.191 < 2e-16 ***
## category_code_LT01_3_count 0.36797 0.11179 3.292 0.00107 **
## category_code_LT01_4_count 0.82877 0.08609 9.626 < 2e-16 ***
## category_code_LT01_5_count 0.91392 0.06168 14.817 < 2e-16 ***
## category_code_LT01_7_count 0.49854 0.15290 3.261 0.00119 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 493 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6256
## F-statistic: 208.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.617766168911161
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0236 -0.7835 0.0351 0.8594 3.4186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99389 0.08769 113.974 <2e-16 ***
## category_code_LT01_3_count 0.38962 0.11280 3.454 0.0006 ***
## category_code_LT01_4_count 0.92948 0.08126 11.438 <2e-16 ***
## category_code_LT01_5_count 0.93029 0.06293 14.784 <2e-16 ***
## category_code_LT01_8_count -0.14899 0.27564 -0.541 0.5891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6178
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.620320209839819
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7690 0.0467 0.8843 3.4397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98749 0.08739 114.284 <2e-16 ***
## category_code_LT01_3_count 0.35379 0.11386 3.107 0.002 **
## category_code_LT01_4_count 0.91053 0.08157 11.163 <2e-16 ***
## category_code_LT01_5_count 0.91689 0.06217 14.748 <2e-16 ***
## category_code_LT01_9_count 0.43121 0.22694 1.900 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 493 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6203
## F-statistic: 204 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.618250332302444
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9952 -0.7588 0.0489 0.8718 3.3289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96921 0.09089 109.680 < 2e-16 ***
## category_code_LT01_3_count 0.36783 0.11471 3.207 0.00143 **
## category_code_LT01_4_count 0.92435 0.08135 11.362 < 2e-16 ***
## category_code_LT01_5_count 0.92499 0.06219 14.875 < 2e-16 ***
## category_code_LT01_10_count 0.10945 0.11425 0.958 0.33853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6183
## F-statistic: 202.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.626864459377303
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0184 -0.7594 0.0457 0.8769 3.4289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99741 0.08661 115.433 < 2e-16 ***
## category_code_LT01_3_count 0.28145 0.11551 2.437 0.015178 *
## category_code_LT01_4_count 0.76289 0.09320 8.186 2.33e-15 ***
## category_code_LT01_5_count 0.91769 0.06152 14.918 < 2e-16 ***
## category_code_LT01_11_count 0.39618 0.11287 3.510 0.000489 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 493 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6269
## F-statistic: 209.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.617933880705064
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0148 -0.7712 0.0356 0.8763 3.4311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99153 0.08764 114.011 < 2e-16 ***
## category_code_LT01_3_count 0.38142 0.11316 3.371 0.000809 ***
## category_code_LT01_4_count 0.91898 0.08245 11.147 < 2e-16 ***
## category_code_LT01_5_count 0.92107 0.06248 14.741 < 2e-16 ***
## category_code_LT01_12_count 0.14700 0.20610 0.713 0.476045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6179
## F-statistic: 202 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.617741698237378
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7750 0.0368 0.8606 3.4267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99224 0.08765 114.002 < 2e-16 ***
## category_code_LT01_3_count 0.38712 0.11280 3.432 0.00065 ***
## category_code_LT01_4_count 0.92306 0.08209 11.245 < 2e-16 ***
## category_code_LT01_5_count 0.92442 0.06225 14.851 < 2e-16 ***
## category_code_LT01_13_count 0.12483 0.24455 0.510 0.60995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6177
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.617800567462784
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0180 -0.7586 0.0375 0.8651 3.4279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99444 0.08771 113.951 < 2e-16 ***
## category_code_LT01_3_count 0.39089 0.11286 3.464 0.00058 ***
## category_code_LT01_4_count 0.91815 0.08338 11.012 < 2e-16 ***
## category_code_LT01_5_count 0.92135 0.06258 14.723 < 2e-16 ***
## category_code_LT01_14_count 0.19107 0.32936 0.580 0.56209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6178
## F-statistic: 201.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.617539775362402
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0186 -0.7812 0.0321 0.8610 3.4256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.992460 0.087674 113.972 < 2e-16 ***
## category_code_LT01_3_count 0.388480 0.113798 3.414 0.000694 ***
## category_code_LT01_4_count 0.929075 0.081513 11.398 < 2e-16 ***
## category_code_LT01_5_count 0.925207 0.062263 14.860 < 2e-16 ***
## category_code_LT01_15_count -0.009412 0.759358 -0.012 0.990116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6175
## F-statistic: 201.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.617875338283374
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0186 -0.7778 0.0305 0.8601 3.4260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99291 0.08764 114.027 < 2e-16 ***
## category_code_LT01_3_count 0.37890 0.11366 3.334 0.000922 ***
## category_code_LT01_4_count 0.92994 0.08126 11.444 < 2e-16 ***
## category_code_LT01_5_count 0.92443 0.06223 14.856 < 2e-16 ***
## category_code_LT01_16_count 0.77514 1.17786 0.658 0.510788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6179
## F-statistic: 201.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.625915399477166
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0027 -0.7605 0.0062 0.9143 4.0530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98889 0.08676 115.129 < 2e-16 ***
## category_code_LT01_4_count 0.87073 0.07947 10.957 < 2e-16 ***
## category_code_LT01_5_count 0.90740 0.06184 14.673 < 2e-16 ***
## category_code_LT01_6_count 0.49932 0.14891 3.353 0.000861 ***
## category_code_LT01_7_count 0.51648 0.15263 3.384 0.000772 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 493 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6259
## F-statistic: 208.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.617504785082986
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.7812 0.0283 0.9147 4.0502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99165 0.08777 113.843 < 2e-16 ***
## category_code_LT01_4_count 0.98189 0.07322 13.410 < 2e-16 ***
## category_code_LT01_5_count 0.92519 0.06310 14.662 < 2e-16 ***
## category_code_LT01_6_count 0.51282 0.15067 3.404 0.000719 ***
## category_code_LT01_8_count -0.16516 0.27588 -0.599 0.549667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6175
## F-statistic: 201.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.620900483142205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9988 -0.7731 0.0324 0.9152 4.0584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98353 0.08739 114.236 < 2e-16 ***
## category_code_LT01_4_count 0.94878 0.07442 12.750 < 2e-16 ***
## category_code_LT01_5_count 0.90941 0.06233 14.591 < 2e-16 ***
## category_code_LT01_6_count 0.48523 0.15028 3.229 0.00133 **
## category_code_LT01_9_count 0.49063 0.22446 2.186 0.02930 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 493 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6209
## F-statistic: 204.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.618037540995909
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9877 -0.7773 -0.0024 0.9041 3.9484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96542 0.09094 109.580 < 2e-16 ***
## category_code_LT01_4_count 0.97314 0.07363 13.217 < 2e-16 ***
## category_code_LT01_5_count 0.91963 0.06238 14.742 < 2e-16 ***
## category_code_LT01_6_count 0.48279 0.15266 3.163 0.00166 **
## category_code_LT01_10_count 0.11655 0.11393 1.023 0.30681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.618
## F-statistic: 202 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.627693959844438
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0110 -0.7448 0.0137 0.9600 3.7617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99429 0.08656 115.458 < 2e-16 ***
## category_code_LT01_4_count 0.78456 0.08954 8.762 < 2e-16 ***
## category_code_LT01_5_count 0.91155 0.06163 14.791 < 2e-16 ***
## category_code_LT01_6_count 0.40173 0.15131 2.655 0.00819 **
## category_code_LT01_11_count 0.41250 0.11080 3.723 0.00022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 493 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6277
## F-statistic: 210.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.617477911903527
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0098 -0.7863 0.0040 0.9485 4.0523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98960 0.08774 113.855 < 2e-16 ***
## category_code_LT01_4_count 0.97368 0.07451 13.068 < 2e-16 ***
## category_code_LT01_5_count 0.91661 0.06266 14.628 < 2e-16 ***
## category_code_LT01_6_count 0.49806 0.15184 3.280 0.00111 **
## category_code_LT01_12_count 0.11791 0.20723 0.569 0.56962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6175
## F-statistic: 201.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.617515952746894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0114 -0.7793 0.0060 0.9264 4.0521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98977 0.08773 113.866 < 2e-16 ***
## category_code_LT01_4_count 0.97362 0.07436 13.094 < 2e-16 ***
## category_code_LT01_5_count 0.91860 0.06245 14.709 < 2e-16 ***
## category_code_LT01_6_count 0.51011 0.15055 3.388 0.00076 ***
## category_code_LT01_13_count 0.14934 0.24458 0.611 0.54176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6175
## F-statistic: 201.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.617651364690023
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.7820 0.0141 0.9163 4.0495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99241 0.08777 113.850 < 2e-16 ***
## category_code_LT01_4_count 0.96685 0.07585 12.748 < 2e-16 ***
## category_code_LT01_5_count 0.91441 0.06282 14.556 < 2e-16 ***
## category_code_LT01_6_count 0.51899 0.15109 3.435 0.000643 ***
## category_code_LT01_14_count 0.24449 0.33042 0.740 0.459675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6177
## F-statistic: 201.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.617277413363697
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0128 -0.7798 -0.0026 0.9197 4.0517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99020 0.08776 113.838 < 2e-16 ***
## category_code_LT01_4_count 0.97879 0.07402 13.223 < 2e-16 ***
## category_code_LT01_5_count 0.91998 0.06246 14.730 < 2e-16 ***
## category_code_LT01_6_count 0.50717 0.15082 3.363 0.000832 ***
## category_code_LT01_15_count 0.19273 0.75415 0.256 0.798400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6204, Adjusted R-squared: 0.6173
## F-statistic: 201.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.618524310003523
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0098 -0.7791 -0.0023 0.9463 4.0523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98955 0.08762 114.016 < 2e-16 ***
## category_code_LT01_4_count 0.97249 0.07345 13.239 < 2e-16 ***
## category_code_LT01_5_count 0.91663 0.06239 14.693 < 2e-16 ***
## category_code_LT01_6_count 0.52116 0.15063 3.460 0.000587 ***
## category_code_LT01_16_count 1.51475 1.16972 1.295 0.195937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 493 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6185
## F-statistic: 202.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.617641401930323
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0464 -0.7606 0.0050 0.8582 4.0297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01219 0.08751 114.405 < 2e-16 ***
## category_code_LT01_4_count 0.98574 0.07255 13.586 < 2e-16 ***
## category_code_LT01_5_count 0.93681 0.06281 14.916 < 2e-16 ***
## category_code_LT01_7_count 0.52946 0.15436 3.430 0.000654 ***
## category_code_LT01_8_count -0.15875 0.27577 -0.576 0.565092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6176
## F-statistic: 201.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.620658842003962
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7906 -0.0273 0.8818 4.0384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00346 0.08719 114.725 < 2e-16 ***
## category_code_LT01_4_count 0.95657 0.07358 13.000 < 2e-16 ***
## category_code_LT01_5_count 0.92145 0.06204 14.851 < 2e-16 ***
## category_code_LT01_7_count 0.49146 0.15461 3.179 0.00157 **
## category_code_LT01_9_count 0.46478 0.22530 2.063 0.03964 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 493 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6207
## F-statistic: 204.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.618670614239284
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0080 -0.7668 0.0217 0.8594 3.9039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97841 0.09085 109.832 < 2e-16 ***
## category_code_LT01_4_count 0.97088 0.07330 13.245 < 2e-16 ***
## category_code_LT01_5_count 0.93018 0.06203 14.997 < 2e-16 ***
## category_code_LT01_7_count 0.50915 0.15466 3.292 0.00107 **
## category_code_LT01_10_count 0.14523 0.11262 1.290 0.19781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 493 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6187
## F-statistic: 202.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.626822133397584
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7443 0.0430 0.9295 3.7554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01056 0.08642 115.842 < 2e-16 ***
## category_code_LT01_4_count 0.80246 0.08841 9.077 < 2e-16 ***
## category_code_LT01_5_count 0.92209 0.06141 15.016 < 2e-16 ***
## category_code_LT01_7_count 0.38260 0.15778 2.425 0.015667 *
## category_code_LT01_11_count 0.39801 0.11272 3.531 0.000453 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 493 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6268
## F-statistic: 209.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.618072545142376
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0353 -0.7808 0.0179 0.8722 4.0330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00891 0.08744 114.465 < 2e-16 ***
## category_code_LT01_4_count 0.96861 0.07462 12.981 < 2e-16 ***
## category_code_LT01_5_count 0.92556 0.06238 14.838 < 2e-16 ***
## category_code_LT01_7_count 0.52396 0.15422 3.398 0.000735 ***
## category_code_LT01_12_count 0.19354 0.20535 0.943 0.346396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6181
## F-statistic: 202.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.61740774682873
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0409 -0.7552 0.0057 0.8412 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01052 0.08750 114.406 < 2e-16 ***
## category_code_LT01_4_count 0.98372 0.07309 13.460 < 2e-16 ***
## category_code_LT01_5_count 0.93119 0.06213 14.987 < 2e-16 ***
## category_code_LT01_7_count 0.52338 0.15544 3.367 0.000819 ***
## category_code_LT01_13_count 0.04276 0.24637 0.174 0.862275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6205, Adjusted R-squared: 0.6174
## F-statistic: 201.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.617416495643803
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0410 -0.7695 -0.0022 0.8603 4.0305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01135 0.08757 114.318 < 2e-16 ***
## category_code_LT01_4_count 0.98230 0.07398 13.278 < 2e-16 ***
## category_code_LT01_5_count 0.93015 0.06243 14.899 < 2e-16 ***
## category_code_LT01_7_count 0.52438 0.15471 3.390 0.000756 ***
## category_code_LT01_14_count 0.06716 0.33009 0.203 0.838850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6205, Adjusted R-squared: 0.6174
## F-statistic: 201.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.617598879860301
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0413 -0.7497 -0.0010 0.8605 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01053 0.08748 114.436 < 2e-16 ***
## category_code_LT01_4_count 0.97814 0.07379 13.255 < 2e-16 ***
## category_code_LT01_5_count 0.93178 0.06211 15.002 < 2e-16 ***
## category_code_LT01_7_count 0.52862 0.15433 3.425 0.000666 ***
## category_code_LT01_15_count 0.39595 0.75293 0.526 0.599205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 493 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6176
## F-statistic: 201.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.618276847570247
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0396 -0.7549 0.0007 0.8694 4.0313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01054 0.08740 114.538 < 2e-16 ***
## category_code_LT01_4_count 0.98016 0.07264 13.493 < 2e-16 ***
## category_code_LT01_5_count 0.92938 0.06208 14.971 < 2e-16 ***
## category_code_LT01_7_count 0.52600 0.15415 3.412 0.000697 ***
## category_code_LT01_16_count 1.25391 1.16793 1.074 0.283520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 493 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6183
## F-statistic: 202.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.613116870437303
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0395 -0.7948 0.0132 0.9372 4.0366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00528 0.08810 113.571 <2e-16 ***
## category_code_LT01_4_count 1.05881 0.06697 15.811 <2e-16 ***
## category_code_LT01_5_count 0.93686 0.06327 14.807 <2e-16 ***
## category_code_LT01_8_count -0.15111 0.27741 -0.545 0.5862
## category_code_LT01_9_count 0.54786 0.22628 2.421 0.0158 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 493 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6131
## F-statistic: 197.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.61048408231915
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0154 -0.7861 0.0192 0.9238 3.8713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97397 0.09183 108.610 <2e-16 ***
## category_code_LT01_4_count 1.07918 0.06635 16.265 <2e-16 ***
## category_code_LT01_5_count 0.94714 0.06330 14.962 <2e-16 ***
## category_code_LT01_8_count -0.13851 0.27827 -0.498 0.619
## category_code_LT01_10_count 0.17898 0.11342 1.578 0.115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6105
## F-statistic: 195.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.62248379784428
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0443 -0.7365 0.0248 0.9119 3.7048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01277 0.08696 115.145 < 2e-16 ***
## category_code_LT01_4_count 0.84943 0.08693 9.772 < 2e-16 ***
## category_code_LT01_5_count 0.93290 0.06242 14.945 < 2e-16 ***
## category_code_LT01_8_count -0.10521 0.27393 -0.384 0.701
## category_code_LT01_11_count 0.46783 0.10954 4.271 2.34e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 493 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6225
## F-statistic: 205.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.609326008104904
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0502 -0.8014 -0.0004 0.9389 4.0300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01188 0.08848 113.158 <2e-16 ***
## category_code_LT01_4_count 1.08292 0.06747 16.050 <2e-16 ***
## category_code_LT01_5_count 0.94277 0.06367 14.807 <2e-16 ***
## category_code_LT01_8_count -0.13874 0.27879 -0.498 0.619
## category_code_LT01_12_count 0.21002 0.20780 1.011 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 493 degrees of freedom
## Multiple R-squared: 0.6125, Adjusted R-squared: 0.6093
## F-statistic: 194.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.608754824458646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0549 -0.7740 -0.0127 0.9368 4.0287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01322 0.08853 113.108 <2e-16 ***
## category_code_LT01_4_count 1.09441 0.06624 16.522 <2e-16 ***
## category_code_LT01_5_count 0.94759 0.06347 14.929 <2e-16 ***
## category_code_LT01_8_count -0.11951 0.27927 -0.428 0.669
## category_code_LT01_13_count 0.13578 0.24777 0.548 0.584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 493 degrees of freedom
## Multiple R-squared: 0.6119, Adjusted R-squared: 0.6088
## F-statistic: 194.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.608673650156129
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0559 -0.7955 -0.0124 0.9350 4.0266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01527 0.08861 113.024 <2e-16 ***
## category_code_LT01_4_count 1.09398 0.06713 16.296 <2e-16 ***
## category_code_LT01_5_count 0.94599 0.06376 14.837 <2e-16 ***
## category_code_LT01_8_count -0.13016 0.27887 -0.467 0.641
## category_code_LT01_14_count 0.14819 0.33304 0.445 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 493 degrees of freedom
## Multiple R-squared: 0.6118, Adjusted R-squared: 0.6087
## F-statistic: 194.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.608670843049297
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0564 -0.7804 -0.0120 0.9506 4.0283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01357 0.08853 113.103 <2e-16 ***
## category_code_LT01_4_count 1.09587 0.06621 16.550 <2e-16 ***
## category_code_LT01_5_count 0.94923 0.06345 14.961 <2e-16 ***
## category_code_LT01_8_count -0.12974 0.27886 -0.465 0.642
## category_code_LT01_15_count 0.33577 0.76148 0.441 0.659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 493 degrees of freedom
## Multiple R-squared: 0.6118, Adjusted R-squared: 0.6087
## F-statistic: 194.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.609471624233987
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0552 -0.7774 -0.0138 0.9535 4.0282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01370 0.08844 113.221 <2e-16 ***
## category_code_LT01_4_count 1.09623 0.06509 16.843 <2e-16 ***
## category_code_LT01_5_count 0.94725 0.06339 14.943 <2e-16 ***
## category_code_LT01_8_count -0.14369 0.27890 -0.515 0.607
## category_code_LT01_16_count 1.29879 1.18281 1.098 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 493 degrees of freedom
## Multiple R-squared: 0.6126, Adjusted R-squared: 0.6095
## F-statistic: 194.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.614149092357132
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0025 -0.7814 -0.0050 0.9141 3.9106
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97229 0.09137 109.137 <2e-16 ***
## category_code_LT01_4_count 1.04291 0.06792 15.356 <2e-16 ***
## category_code_LT01_5_count 0.93098 0.06249 14.898 <2e-16 ***
## category_code_LT01_9_count 0.50593 0.22779 2.221 0.0268 *
## category_code_LT01_10_count 0.14471 0.11382 1.271 0.2042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.399 on 493 degrees of freedom
## Multiple R-squared: 0.6173, Adjusted R-squared: 0.6141
## F-statistic: 198.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.625448260988864
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7610 0.0394 0.9405 3.7280
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00471 0.08664 115.471 < 2e-16 ***
## category_code_LT01_4_count 0.82523 0.08728 9.455 < 2e-16 ***
## category_code_LT01_5_count 0.91950 0.06164 14.917 < 2e-16 ***
## category_code_LT01_9_count 0.45022 0.22370 2.013 0.0447 *
## category_code_LT01_11_count 0.44598 0.10967 4.067 5.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 493 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6254
## F-statistic: 208.5 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.613577009570886
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0289 -0.7918 0.0213 0.9447 4.0397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00221 0.08802 113.633 <2e-16 ***
## category_code_LT01_4_count 1.04121 0.06927 15.031 <2e-16 ***
## category_code_LT01_5_count 0.92593 0.06284 14.735 <2e-16 ***
## category_code_LT01_9_count 0.53852 0.22608 2.382 0.0176 *
## category_code_LT01_12_count 0.19424 0.20658 0.940 0.3475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 493 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.6136
## F-statistic: 198.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.613284911885732
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0330 -0.7928 0.0261 0.9309 4.0386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00329 0.08804 113.620 <2e-16 ***
## category_code_LT01_4_count 1.04824 0.06833 15.340 <2e-16 ***
## category_code_LT01_5_count 0.93032 0.06259 14.863 <2e-16 ***
## category_code_LT01_9_count 0.55289 0.22647 2.441 0.015 *
## category_code_LT01_13_count 0.17609 0.24632 0.715 0.475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 493 degrees of freedom
## Multiple R-squared: 0.6164, Adjusted R-squared: 0.6133
## F-statistic: 198 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.612943611304104
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0345 -0.7945 0.0248 0.9123 4.0370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00491 0.08816 113.482 <2e-16 ***
## category_code_LT01_4_count 1.05375 0.06877 15.323 <2e-16 ***
## category_code_LT01_5_count 0.93006 0.06289 14.789 <2e-16 ***
## category_code_LT01_9_count 0.53938 0.22674 2.379 0.0177 *
## category_code_LT01_14_count 0.09145 0.33198 0.275 0.7831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 493 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6129
## F-statistic: 197.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.613060626529397
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.7933 0.0228 0.9229 4.0381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00374 0.08806 113.596 <2e-16 ***
## category_code_LT01_4_count 1.05191 0.06820 15.423 <2e-16 ***
## category_code_LT01_5_count 0.93209 0.06258 14.894 <2e-16 ***
## category_code_LT01_9_count 0.54530 0.22619 2.411 0.0163 *
## category_code_LT01_15_count 0.35919 0.75723 0.474 0.6355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 493 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6131
## F-statistic: 197.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.613617230370163
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0335 -0.7935 0.0137 0.9350 4.0379
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00393 0.08800 113.681 <2e-16 ***
## category_code_LT01_4_count 1.05415 0.06703 15.726 <2e-16 ***
## category_code_LT01_5_count 0.93014 0.06255 14.869 <2e-16 ***
## category_code_LT01_9_count 0.53339 0.22625 2.358 0.0188 *
## category_code_LT01_16_count 1.13778 1.17633 0.967 0.3339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 493 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.6136
## F-statistic: 198.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.623651966892042
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7433 0.0365 0.9341 3.5854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97958 0.09026 110.571 < 2e-16 ***
## category_code_LT01_4_count 0.83550 0.08729 9.572 < 2e-16 ***
## category_code_LT01_5_count 0.92792 0.06161 15.062 < 2e-16 ***
## category_code_LT01_10_count 0.14474 0.11173 1.295 0.196
## category_code_LT01_11_count 0.45865 0.10962 4.184 3.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 493 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6237
## F-statistic: 206.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.610949299236009
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7741 0.0179 0.9281 3.8802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97240 0.09175 108.688 <2e-16 ***
## category_code_LT01_4_count 1.06216 0.06861 15.481 <2e-16 ***
## category_code_LT01_5_count 0.93664 0.06285 14.902 <2e-16 ***
## category_code_LT01_10_count 0.17230 0.11347 1.518 0.130
## category_code_LT01_12_count 0.18990 0.20750 0.915 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6109
## F-statistic: 196.1 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.610506623871506
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0105 -0.7717 0.0239 0.9227 3.8758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97299 0.09180 108.634 <2e-16 ***
## category_code_LT01_4_count 1.07188 0.06746 15.889 <2e-16 ***
## category_code_LT01_5_count 0.94151 0.06260 15.041 <2e-16 ***
## category_code_LT01_10_count 0.17579 0.11344 1.550 0.122
## category_code_LT01_13_count 0.12980 0.24693 0.526 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6105
## F-statistic: 195.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.610297079796145
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0115 -0.7772 0.0261 0.9193 3.8757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97385 0.09230 108.064 <2e-16 ***
## category_code_LT01_4_count 1.07686 0.06784 15.874 <2e-16 ***
## category_code_LT01_5_count 0.94171 0.06292 14.968 <2e-16 ***
## category_code_LT01_10_count 0.17505 0.11612 1.507 0.132
## category_code_LT01_14_count 0.03581 0.34025 0.105 0.916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6103
## F-statistic: 195.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.610362478564015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0118 -0.7739 0.0259 0.9356 3.8764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97348 0.09184 108.594 <2e-16 ***
## category_code_LT01_4_count 1.07480 0.06734 15.960 <2e-16 ***
## category_code_LT01_5_count 0.94267 0.06259 15.060 <2e-16 ***
## category_code_LT01_10_count 0.17475 0.11380 1.536 0.125
## category_code_LT01_15_count 0.23355 0.76247 0.306 0.759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6104
## F-statistic: 195.6 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.611073505513763
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0109 -0.7789 0.0269 0.9394 3.8789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97408 0.09174 108.716 <2e-16 ***
## category_code_LT01_4_count 1.07419 0.06641 16.175 <2e-16 ***
## category_code_LT01_5_count 0.94053 0.06256 15.035 <2e-16 ***
## category_code_LT01_10_count 0.17194 0.11345 1.516 0.130
## category_code_LT01_16_count 1.17763 1.18041 0.998 0.319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 493 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6111
## F-statistic: 196.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.622391763123286
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0416 -0.7343 0.0290 0.9146 3.7015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01202 0.08694 115.154 < 2e-16 ***
## category_code_LT01_4_count 0.84857 0.08691 9.764 < 2e-16 ***
## category_code_LT01_5_count 0.93015 0.06194 15.016 < 2e-16 ***
## category_code_LT01_11_count 0.47380 0.11386 4.161 3.74e-05 ***
## category_code_LT01_12_count -0.03507 0.21223 -0.165 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 493 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6224
## F-statistic: 205.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.622453735805889
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0402 -0.7309 0.0327 0.9116 3.7070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01153 0.08692 115.181 < 2e-16 ***
## category_code_LT01_4_count 0.84524 0.08734 9.677 < 2e-16 ***
## category_code_LT01_5_count 0.92876 0.06171 15.050 < 2e-16 ***
## category_code_LT01_11_count 0.46650 0.10972 4.252 2.54e-05 ***
## category_code_LT01_13_count 0.08008 0.24342 0.329 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 493 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6225
## F-statistic: 205.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.622454099410853
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0404 -0.7315 0.0331 0.9179 3.7048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01290 0.08699 115.100 < 2e-16 ***
## category_code_LT01_4_count 0.84328 0.08816 9.565 < 2e-16 ***
## category_code_LT01_5_count 0.92717 0.06202 14.950 < 2e-16 ***
## category_code_LT01_11_count 0.46767 0.10956 4.269 2.36e-05 ***
## category_code_LT01_14_count 0.10789 0.32721 0.330 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 493 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6225
## F-statistic: 205.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.622390370181452
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0408 -0.7314 0.0318 0.9170 3.7061
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01169 0.08693 115.175 < 2e-16 ***
## category_code_LT01_4_count 0.84684 0.08731 9.699 < 2e-16 ***
## category_code_LT01_5_count 0.92941 0.06171 15.061 < 2e-16 ***
## category_code_LT01_11_count 0.46749 0.10977 4.259 2.46e-05 ***
## category_code_LT01_15_count 0.11969 0.74962 0.160 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 493 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6224
## F-statistic: 205.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.623014185537501
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0395 -0.7305 0.0338 0.9170 3.7082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01166 0.08685 115.270 < 2e-16 ***
## category_code_LT01_4_count 0.84604 0.08684 9.742 < 2e-16 ***
## category_code_LT01_5_count 0.92765 0.06167 15.041 < 2e-16 ***
## category_code_LT01_11_count 0.46452 0.10953 4.241 2.66e-05 ***
## category_code_LT01_16_count 1.06550 1.16164 0.917 0.359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 493 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.623
## F-statistic: 206.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.609365031137928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0448 -0.7954 0.0151 0.9319 4.0316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01024 0.08843 113.197 <2e-16 ***
## category_code_LT01_4_count 1.07559 0.06856 15.689 <2e-16 ***
## category_code_LT01_5_count 0.93728 0.06299 14.879 <2e-16 ***
## category_code_LT01_12_count 0.20285 0.20774 0.976 0.329
## category_code_LT01_13_count 0.13474 0.24727 0.545 0.586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 493 degrees of freedom
## Multiple R-squared: 0.6125, Adjusted R-squared: 0.6094
## F-statistic: 194.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.60923484313435
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0455 -0.7968 -0.0189 0.9226 4.0300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01191 0.08853 113.094 <2e-16 ***
## category_code_LT01_4_count 1.07660 0.06923 15.550 <2e-16 ***
## category_code_LT01_5_count 0.93590 0.06327 14.791 <2e-16 ***
## category_code_LT01_12_count 0.20044 0.20828 0.962 0.336
## category_code_LT01_14_count 0.12152 0.33373 0.364 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 493 degrees of freedom
## Multiple R-squared: 0.6124, Adjusted R-squared: 0.6092
## F-statistic: 194.7 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609289314947603
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0458 -0.7979 0.0110 0.9376 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01044 0.08844 113.190 <2e-16 ***
## category_code_LT01_4_count 1.07643 0.06870 15.668 <2e-16 ***
## category_code_LT01_5_count 0.93843 0.06298 14.900 <2e-16 ***
## category_code_LT01_12_count 0.20739 0.20768 0.999 0.318
## category_code_LT01_15_count 0.34142 0.76089 0.449 0.654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 493 degrees of freedom
## Multiple R-squared: 0.6124, Adjusted R-squared: 0.6093
## F-statistic: 194.8 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610040051797881
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0442 -0.7993 0.0085 0.9383 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01045 0.08835 113.299 <2e-16 ***
## category_code_LT01_4_count 1.07705 0.06757 15.939 <2e-16 ***
## category_code_LT01_5_count 0.93606 0.06295 14.870 <2e-16 ***
## category_code_LT01_12_count 0.20585 0.20747 0.992 0.322
## category_code_LT01_16_count 1.26636 1.18046 1.073 0.284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 493 degrees of freedom
## Multiple R-squared: 0.6132, Adjusted R-squared: 0.61
## F-statistic: 195.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.60876233399867
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0504 -0.7751 -0.0017 0.9179 4.0283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01361 0.08856 113.074 <2e-16 ***
## category_code_LT01_4_count 1.08582 0.06831 15.895 <2e-16 ***
## category_code_LT01_5_count 0.94056 0.06306 14.915 <2e-16 ***
## category_code_LT01_13_count 0.14202 0.24735 0.574 0.566
## category_code_LT01_14_count 0.14613 0.33297 0.439 0.661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 493 degrees of freedom
## Multiple R-squared: 0.6119, Adjusted R-squared: 0.6088
## F-statistic: 194.3 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.608786357957881
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0510 -0.7803 0.0027 0.9250 4.0300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01192 0.08848 113.155 <2e-16 ***
## category_code_LT01_4_count 1.08683 0.06756 16.086 <2e-16 ***
## category_code_LT01_5_count 0.94376 0.06273 15.046 <2e-16 ***
## category_code_LT01_13_count 0.14912 0.24782 0.602 0.548
## category_code_LT01_15_count 0.36012 0.76278 0.472 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 493 degrees of freedom
## Multiple R-squared: 0.6119, Adjusted R-squared: 0.6088
## F-statistic: 194.4 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609556182758958
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0492 -0.7768 0.0034 0.9270 4.0300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01191 0.08839 113.267 <2e-16 ***
## category_code_LT01_4_count 1.08743 0.06633 16.394 <2e-16 ***
## category_code_LT01_5_count 0.94126 0.06269 15.014 <2e-16 ***
## category_code_LT01_13_count 0.15085 0.24724 0.610 0.542
## category_code_LT01_16_count 1.29214 1.18184 1.093 0.275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 493 degrees of freedom
## Multiple R-squared: 0.6127, Adjusted R-squared: 0.6096
## F-statistic: 195 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.60864793009894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0516 -0.7991 -0.0003 0.9245 4.0280
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01386 0.08857 113.063 <2e-16 ***
## category_code_LT01_4_count 1.08775 0.06828 15.931 <2e-16 ***
## category_code_LT01_5_count 0.94193 0.06305 14.938 <2e-16 ***
## category_code_LT01_14_count 0.14424 0.33304 0.433 0.665
## category_code_LT01_15_count 0.32791 0.76150 0.431 0.667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 493 degrees of freedom
## Multiple R-squared: 0.6118, Adjusted R-squared: 0.6086
## F-statistic: 194.2 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609452081426461
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0499 -0.7974 0.0004 0.9266 4.0278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01406 0.08848 113.181 <2e-16 ***
## category_code_LT01_4_count 1.08691 0.06728 16.154 <2e-16 ***
## category_code_LT01_5_count 0.93910 0.06302 14.901 <2e-16 ***
## category_code_LT01_14_count 0.16342 0.33306 0.491 0.624
## category_code_LT01_16_count 1.29609 1.18271 1.096 0.274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 493 degrees of freedom
## Multiple R-squared: 0.6126, Adjusted R-squared: 0.6095
## F-statistic: 194.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609432744070072
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0505 -0.7821 0.0022 0.9386 4.0297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01220 0.08840 113.254 <2e-16 ***
## category_code_LT01_4_count 1.08933 0.06633 16.423 <2e-16 ***
## category_code_LT01_5_count 0.94270 0.06268 15.040 <2e-16 ***
## category_code_LT01_15_count 0.35393 0.76096 0.465 0.642
## category_code_LT01_16_count 1.28301 1.18180 1.086 0.278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 493 degrees of freedom
## Multiple R-squared: 0.6126, Adjusted R-squared: 0.6094
## F-statistic: 194.9 on 4 and 493 DF, p-value: < 2.2e-16
##
## ########################################
## i: 5
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count 0.645301168218621
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9540 -0.7184 0.0543 0.8656 3.4909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92880 0.08514 116.623 < 2e-16 ***
## category_code_LT01_1_count 0.23463 0.08668 2.707 0.00703 **
## category_code_LT01_2_count 0.48510 0.08904 5.448 8.07e-08 ***
## category_code_LT01_3_count 0.20443 0.11250 1.817 0.06981 .
## category_code_LT01_4_count 0.52636 0.10128 5.197 2.97e-07 ***
## category_code_LT01_5_count 0.92374 0.06017 15.352 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.341 on 492 degrees of freedom
## Multiple R-squared: 0.6489, Adjusted R-squared: 0.6453
## F-statistic: 181.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count 0.630340073671924
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9715 -0.7911 0.0454 0.8471 3.4693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92381 0.08700 114.073 < 2e-16 ***
## category_code_LT01_1_count 0.35788 0.08422 4.250 2.56e-05 ***
## category_code_LT01_2_count 0.66006 0.08212 8.038 6.85e-15 ***
## category_code_LT01_3_count 0.34719 0.11042 3.144 0.00177 **
## category_code_LT01_5_count 0.95629 0.06109 15.654 < 2e-16 ***
## category_code_LT01_6_count 0.37081 0.15135 2.450 0.01463 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 492 degrees of freedom
## Multiple R-squared: 0.6341, Adjusted R-squared: 0.6303
## F-statistic: 170.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count 0.634041502072783
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7397 0.0346 0.8075 3.4555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93762 0.08648 114.910 < 2e-16 ***
## category_code_LT01_1_count 0.33922 0.08421 4.028 6.51e-05 ***
## category_code_LT01_2_count 0.65651 0.08005 8.201 2.09e-15 ***
## category_code_LT01_3_count 0.34895 0.10955 3.185 0.001538 **
## category_code_LT01_5_count 0.95760 0.06052 15.824 < 2e-16 ***
## category_code_LT01_7_count 0.49865 0.15008 3.323 0.000958 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.362 on 492 degrees of freedom
## Multiple R-squared: 0.6377, Adjusted R-squared: 0.634
## F-statistic: 173.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count 0.626060395649346
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7551 0.0482 0.8294 3.4585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93469 0.08744 113.612 < 2e-16 ***
## category_code_LT01_1_count 0.38248 0.08422 4.541 7.04e-06 ***
## category_code_LT01_2_count 0.72419 0.07822 9.258 < 2e-16 ***
## category_code_LT01_3_count 0.37717 0.11046 3.414 0.000692 ***
## category_code_LT01_5_count 0.98085 0.06166 15.907 < 2e-16 ***
## category_code_LT01_8_count -0.15018 0.27277 -0.551 0.582172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6261
## F-statistic: 167.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count 0.627259644681674
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9880 -0.7780 0.0645 0.8096 3.4620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93119 0.08728 113.781 < 2e-16 ***
## category_code_LT01_1_count 0.37634 0.08410 4.475 9.52e-06 ***
## category_code_LT01_2_count 0.70983 0.07884 9.004 < 2e-16 ***
## category_code_LT01_3_count 0.35438 0.11136 3.182 0.00155 **
## category_code_LT01_5_count 0.96946 0.06099 15.894 < 2e-16 ***
## category_code_LT01_9_count 0.31074 0.22621 1.374 0.17016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6273
## F-statistic: 168.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count 0.626349490383682
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9746 -0.7635 0.0271 0.8226 3.4796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91354 0.09060 109.424 < 2e-16 ***
## category_code_LT01_1_count 0.38339 0.08419 4.554 6.65e-06 ***
## category_code_LT01_2_count 0.71851 0.07854 9.148 < 2e-16 ***
## category_code_LT01_3_count 0.35868 0.11233 3.193 0.0015 **
## category_code_LT01_5_count 0.97553 0.06090 16.018 < 2e-16 ***
## category_code_LT01_10_count 0.09374 0.11335 0.827 0.4086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6263
## F-statistic: 167.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count 0.63028562471899
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9999 -0.7766 0.0651 0.8285 3.4471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94607 0.08707 114.229 < 2e-16 ***
## category_code_LT01_1_count 0.33457 0.08583 3.898 0.000111 ***
## category_code_LT01_2_count 0.61854 0.08916 6.938 1.26e-11 ***
## category_code_LT01_3_count 0.30815 0.11328 2.720 0.006752 **
## category_code_LT01_5_count 0.96511 0.06074 15.890 < 2e-16 ***
## category_code_LT01_11_count 0.28765 0.11813 2.435 0.015245 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 492 degrees of freedom
## Multiple R-squared: 0.634, Adjusted R-squared: 0.6303
## F-statistic: 170.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count 0.625853790760832
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7778 0.0480 0.8303 3.4599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93324 0.08744 113.606 < 2e-16 ***
## category_code_LT01_1_count 0.38281 0.08487 4.511 8.09e-06 ***
## category_code_LT01_2_count 0.72695 0.07926 9.171 < 2e-16 ***
## category_code_LT01_3_count 0.37680 0.11060 3.407 0.000711 ***
## category_code_LT01_5_count 0.97674 0.06127 15.942 < 2e-16 ***
## category_code_LT01_12_count -0.03661 0.20699 -0.177 0.859676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6259
## F-statistic: 167.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count 0.625841213245675
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9944 -0.7730 0.0482 0.8312 3.4597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93347 0.08744 113.602 < 2e-16 ***
## category_code_LT01_1_count 0.37930 0.08526 4.449 1.07e-05 ***
## category_code_LT01_2_count 0.72407 0.07841 9.234 < 2e-16 ***
## category_code_LT01_3_count 0.37576 0.11047 3.401 0.000725 ***
## category_code_LT01_5_count 0.97531 0.06100 15.989 < 2e-16 ***
## category_code_LT01_13_count 0.02966 0.24423 0.121 0.903397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6258
## F-statistic: 167.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count 0.626029752359783
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9940 -0.7701 0.0464 0.8287 3.4574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93571 0.08753 113.506 < 2e-16 ***
## category_code_LT01_1_count 0.37459 0.08508 4.403 1.31e-05 ***
## category_code_LT01_2_count 0.71942 0.07889 9.119 < 2e-16 ***
## category_code_LT01_3_count 0.37781 0.11051 3.419 0.000681 ***
## category_code_LT01_5_count 0.97149 0.06146 15.807 < 2e-16 ***
## category_code_LT01_14_count 0.16725 0.32626 0.513 0.608434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.626
## F-statistic: 167.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count 0.62622374799973
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9923 -0.7800 0.0711 0.8306 3.4614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93171 0.08742 113.609 < 2e-16 ***
## category_code_LT01_1_count 0.39100 0.08531 4.583 5.81e-06 ***
## category_code_LT01_2_count 0.72585 0.07822 9.280 < 2e-16 ***
## category_code_LT01_3_count 0.38399 0.11099 3.460 0.000588 ***
## category_code_LT01_5_count 0.97489 0.06092 16.002 < 2e-16 ***
## category_code_LT01_15_count -0.54780 0.76091 -0.720 0.471914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6262
## F-statistic: 167.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_16_count 0.625830092682591
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9945 -0.7744 0.0483 0.8309 3.4598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93333 0.08744 113.597 < 2e-16 ***
## category_code_LT01_1_count 0.38087 0.08435 4.516 7.91e-06 ***
## category_code_LT01_2_count 0.72477 0.07850 9.233 < 2e-16 ***
## category_code_LT01_3_count 0.37596 0.11096 3.388 0.00076 ***
## category_code_LT01_5_count 0.97563 0.06095 16.008 < 2e-16 ***
## category_code_LT01_16_count -0.01299 1.16993 -0.011 0.99114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6258
## F-statistic: 167.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.645443215095747
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9495 -0.7639 0.0832 0.8551 3.4966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92647 0.08516 116.557 < 2e-16 ***
## category_code_LT01_1_count 0.24229 0.08626 2.809 0.00517 **
## category_code_LT01_2_count 0.48265 0.08919 5.411 9.79e-08 ***
## category_code_LT01_4_count 0.54923 0.09823 5.592 3.74e-08 ***
## category_code_LT01_5_count 0.92068 0.06031 15.267 < 2e-16 ***
## category_code_LT01_6_count 0.27978 0.14954 1.871 0.06195 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.341 on 492 degrees of freedom
## Multiple R-squared: 0.649, Adjusted R-squared: 0.6454
## F-statistic: 182 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.647180319459494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9629 -0.7268 0.0517 0.8255 3.4816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93720 0.08488 117.075 < 2e-16 ***
## category_code_LT01_1_count 0.23576 0.08615 2.737 0.00643 **
## category_code_LT01_2_count 0.49258 0.08737 5.638 2.91e-08 ***
## category_code_LT01_4_count 0.53027 0.09873 5.371 1.21e-07 ***
## category_code_LT01_5_count 0.92453 0.05990 15.436 < 2e-16 ***
## category_code_LT01_7_count 0.36616 0.15024 2.437 0.01516 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.338 on 492 degrees of freedom
## Multiple R-squared: 0.6507, Adjusted R-squared: 0.6472
## F-statistic: 183.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.643139485364286
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9703 -0.7502 0.0775 0.8481 3.4687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93506 0.08539 116.355 < 2e-16 ***
## category_code_LT01_1_count 0.25466 0.08639 2.948 0.00335 **
## category_code_LT01_2_count 0.52061 0.08709 5.978 4.35e-09 ***
## category_code_LT01_4_count 0.58087 0.09713 5.980 4.29e-09 ***
## category_code_LT01_5_count 0.93823 0.06085 15.418 < 2e-16 ***
## category_code_LT01_8_count -0.14631 0.26642 -0.549 0.58315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 492 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6431
## F-statistic: 180.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.644547286058996
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9577 -0.7221 0.0925 0.8756 3.4863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93096 0.08520 116.557 < 2e-16 ***
## category_code_LT01_1_count 0.24733 0.08626 2.867 0.00432 **
## category_code_LT01_2_count 0.50316 0.08772 5.736 1.70e-08 ***
## category_code_LT01_4_count 0.57094 0.09714 5.878 7.68e-09 ***
## category_code_LT01_5_count 0.92596 0.06020 15.380 < 2e-16 ***
## category_code_LT01_9_count 0.32885 0.21917 1.500 0.13414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 492 degrees of freedom
## Multiple R-squared: 0.6481, Adjusted R-squared: 0.6445
## F-statistic: 181.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.64368172759474
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9408 -0.7481 0.1049 0.8741 3.3778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90964 0.08846 112.021 < 2e-16 ***
## category_code_LT01_1_count 0.25492 0.08629 2.954 0.00329 **
## category_code_LT01_2_count 0.51124 0.08754 5.840 9.49e-09 ***
## category_code_LT01_4_count 0.57274 0.09733 5.884 7.40e-09 ***
## category_code_LT01_5_count 0.93232 0.06009 15.515 < 2e-16 ***
## category_code_LT01_10_count 0.11183 0.10910 1.025 0.30584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 492 degrees of freedom
## Multiple R-squared: 0.6473, Adjusted R-squared: 0.6437
## F-statistic: 180.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.644823850538977
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9701 -0.7387 0.0544 0.8463 3.4728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94169 0.08529 116.562 < 2e-16 ***
## category_code_LT01_1_count 0.23019 0.08729 2.637 0.00862 **
## category_code_LT01_2_count 0.46260 0.09403 4.920 1.18e-06 ***
## category_code_LT01_4_count 0.53347 0.10110 5.277 1.98e-07 ***
## category_code_LT01_5_count 0.92845 0.06006 15.459 < 2e-16 ***
## category_code_LT01_11_count 0.19015 0.11711 1.624 0.10509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 492 degrees of freedom
## Multiple R-squared: 0.6484, Adjusted R-squared: 0.6448
## F-statistic: 181.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.642963768395988
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9663 -0.7609 0.0779 0.8462 3.4742
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93360 0.08537 116.354 < 2e-16 ***
## category_code_LT01_1_count 0.25556 0.08695 2.939 0.00345 **
## category_code_LT01_2_count 0.52392 0.08793 5.958 4.86e-09 ***
## category_code_LT01_4_count 0.58124 0.09722 5.978 4.33e-09 ***
## category_code_LT01_5_count 0.93459 0.06046 15.459 < 2e-16 ***
## category_code_LT01_12_count -0.04921 0.20211 -0.243 0.80774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 492 degrees of freedom
## Multiple R-squared: 0.6466, Adjusted R-squared: 0.643
## F-statistic: 180 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.642929069450835
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9654 -0.7698 0.0794 0.8547 3.4756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93361 0.08538 116.342 < 2e-16 ***
## category_code_LT01_1_count 0.25437 0.08717 2.918 0.00368 **
## category_code_LT01_2_count 0.52131 0.08716 5.981 4.27e-09 ***
## category_code_LT01_4_count 0.58075 0.09724 5.972 4.49e-09 ***
## category_code_LT01_5_count 0.93332 0.06019 15.507 < 2e-16 ***
## category_code_LT01_13_count -0.02557 0.23880 -0.107 0.91477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 492 degrees of freedom
## Multiple R-squared: 0.6465, Adjusted R-squared: 0.6429
## F-statistic: 180 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.642939564081262
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9653 -0.7524 0.0790 0.8621 3.4753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93295 0.08551 116.161 < 2e-16 ***
## category_code_LT01_1_count 0.25445 0.08677 2.932 0.00352 **
## category_code_LT01_2_count 0.52159 0.08719 5.982 4.24e-09 ***
## category_code_LT01_4_count 0.58180 0.09759 5.962 4.77e-09 ***
## category_code_LT01_5_count 0.93415 0.06050 15.441 < 2e-16 ***
## category_code_LT01_14_count -0.05153 0.32003 -0.161 0.87215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 492 degrees of freedom
## Multiple R-squared: 0.6465, Adjusted R-squared: 0.6429
## F-statistic: 180 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.643084909817089
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9644 -0.7504 0.0793 0.8455 3.4768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93281 0.08538 116.337 < 2e-16 ***
## category_code_LT01_1_count 0.26026 0.08765 2.969 0.00313 **
## category_code_LT01_2_count 0.52300 0.08720 5.998 3.87e-09 ***
## category_code_LT01_4_count 0.58107 0.09715 5.981 4.26e-09 ***
## category_code_LT01_5_count 0.93295 0.06014 15.513 < 2e-16 ***
## category_code_LT01_15_count -0.35191 0.73977 -0.476 0.63450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 492 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6431
## F-statistic: 180.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.643066199955331
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9653 -0.7589 0.0803 0.8637 3.4760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93405 0.08536 116.374 < 2e-16 ***
## category_code_LT01_1_count 0.25413 0.08638 2.942 0.00342 **
## category_code_LT01_2_count 0.51622 0.08775 5.883 7.44e-09 ***
## category_code_LT01_4_count 0.58127 0.09716 5.983 4.23e-09 ***
## category_code_LT01_5_count 0.93252 0.06015 15.502 < 2e-16 ***
## category_code_LT01_16_count 0.50950 1.13789 0.448 0.65452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 492 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6431
## F-statistic: 180.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.632150161920961
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9865 -0.7742 0.0390 0.9143 3.4568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93637 0.08675 114.545 < 2e-16 ***
## category_code_LT01_1_count 0.36765 0.08310 4.424 1.19e-05 ***
## category_code_LT01_2_count 0.68266 0.07856 8.690 < 2e-16 ***
## category_code_LT01_5_count 0.95982 0.06074 15.803 < 2e-16 ***
## category_code_LT01_6_count 0.41294 0.15015 2.750 0.00617 **
## category_code_LT01_7_count 0.52751 0.15007 3.515 0.00048 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 492 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6322
## F-statistic: 171.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.623176387020662
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0022 -0.8020 0.0319 0.9492 3.4593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93381 0.08782 113.113 < 2e-16 ***
## category_code_LT01_1_count 0.41787 0.08299 5.035 6.7e-07 ***
## category_code_LT01_2_count 0.76155 0.07614 10.002 < 2e-16 ***
## category_code_LT01_5_count 0.98614 0.06189 15.933 < 2e-16 ***
## category_code_LT01_6_count 0.42482 0.15207 2.794 0.00542 **
## category_code_LT01_8_count -0.16087 0.27398 -0.587 0.55738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6232
## F-statistic: 165.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.625113574654028
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9878 -0.7962 0.0813 0.9429 3.4638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92937 0.08758 113.371 < 2e-16 ***
## category_code_LT01_1_count 0.40682 0.08291 4.907 1.26e-06 ***
## category_code_LT01_2_count 0.73803 0.07727 9.551 < 2e-16 ***
## category_code_LT01_5_count 0.97171 0.06125 15.865 < 2e-16 ***
## category_code_LT01_6_count 0.40862 0.15174 2.693 0.00732 **
## category_code_LT01_9_count 0.38222 0.22488 1.700 0.08982 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6251
## F-statistic: 166.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.623680587574491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.8077 0.0213 0.9594 3.4846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90854 0.09091 108.990 < 2e-16 ***
## category_code_LT01_1_count 0.41738 0.08289 5.035 6.71e-07 ***
## category_code_LT01_2_count 0.75310 0.07663 9.827 < 2e-16 ***
## category_code_LT01_5_count 0.98034 0.06114 16.034 < 2e-16 ***
## category_code_LT01_6_count 0.39633 0.15386 2.576 0.0103 *
## category_code_LT01_10_count 0.11352 0.11328 1.002 0.3167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 492 degrees of freedom
## Multiple R-squared: 0.6275, Adjusted R-squared: 0.6237
## F-statistic: 165.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.628932657223291
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0010 -0.7950 0.0579 0.8726 3.4468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94640 0.08726 113.989 < 2e-16 ***
## category_code_LT01_1_count 0.35432 0.08517 4.160 3.76e-05 ***
## category_code_LT01_2_count 0.62880 0.08912 7.056 5.86e-12 ***
## category_code_LT01_5_count 0.96618 0.06093 15.858 < 2e-16 ***
## category_code_LT01_6_count 0.35983 0.15234 2.362 0.01856 *
## category_code_LT01_11_count 0.32748 0.11591 2.825 0.00492 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 492 degrees of freedom
## Multiple R-squared: 0.6327, Adjusted R-squared: 0.6289
## F-statistic: 169.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.622971905856743
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9979 -0.8118 0.0348 0.9522 3.4610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93219 0.08782 113.099 < 2e-16 ***
## category_code_LT01_1_count 0.41919 0.08364 5.012 7.52e-07 ***
## category_code_LT01_2_count 0.76560 0.07703 9.939 < 2e-16 ***
## category_code_LT01_5_count 0.98229 0.06148 15.977 < 2e-16 ***
## category_code_LT01_6_count 0.42532 0.15271 2.785 0.00556 **
## category_code_LT01_12_count -0.05813 0.20851 -0.279 0.78053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.623
## F-statistic: 165.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.622944437370441
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9968 -0.8075 0.0430 0.9366 3.4605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93261 0.08782 113.099 < 2e-16 ***
## category_code_LT01_1_count 0.41340 0.08411 4.915 1.21e-06 ***
## category_code_LT01_2_count 0.76111 0.07640 9.963 < 2e-16 ***
## category_code_LT01_5_count 0.98006 0.06126 15.998 < 2e-16 ***
## category_code_LT01_6_count 0.42192 0.15204 2.775 0.00573 **
## category_code_LT01_13_count 0.05018 0.24524 0.205 0.83795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6229
## F-statistic: 165.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.623235422614448
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9960 -0.8124 0.0464 0.9570 3.4579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93527 0.08789 113.039 < 2e-16 ***
## category_code_LT01_1_count 0.40790 0.08392 4.861 1.58e-06 ***
## category_code_LT01_2_count 0.75463 0.07705 9.794 < 2e-16 ***
## category_code_LT01_5_count 0.97503 0.06178 15.782 < 2e-16 ***
## category_code_LT01_6_count 0.43029 0.15258 2.820 0.00499 **
## category_code_LT01_14_count 0.21348 0.32867 0.650 0.51630
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 492 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6232
## F-statistic: 165.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.623071054085462
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9960 -0.8085 0.0435 0.9468 3.4617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93149 0.08783 113.080 < 2e-16 ***
## category_code_LT01_1_count 0.42336 0.08441 5.015 7.41e-07 ***
## category_code_LT01_2_count 0.76432 0.07626 10.023 < 2e-16 ***
## category_code_LT01_5_count 0.98046 0.06119 16.023 < 2e-16 ***
## category_code_LT01_6_count 0.42339 0.15205 2.785 0.00557 **
## category_code_LT01_15_count -0.34614 0.76051 -0.455 0.64921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6269, Adjusted R-squared: 0.6231
## F-statistic: 165.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.623160257565
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9965 -0.8025 0.0460 0.9492 3.4605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93265 0.08779 113.137 < 2e-16 ***
## category_code_LT01_1_count 0.41739 0.08297 5.031 6.86e-07 ***
## category_code_LT01_2_count 0.75519 0.07716 9.787 < 2e-16 ***
## category_code_LT01_5_count 0.97956 0.06121 16.003 < 2e-16 ***
## category_code_LT01_6_count 0.42930 0.15262 2.813 0.00511 **
## category_code_LT01_16_count 0.66792 1.17403 0.569 0.56967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6232
## F-statistic: 165.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.626758942541385
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0200 -0.7727 0.0264 0.9165 3.4437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94945 0.08731 113.95 < 2e-16 ***
## category_code_LT01_1_count 0.40007 0.08299 4.82 1.91e-06 ***
## category_code_LT01_2_count 0.76344 0.07332 10.41 < 2e-16 ***
## category_code_LT01_5_count 0.98918 0.06129 16.14 < 2e-16 ***
## category_code_LT01_7_count 0.53683 0.15123 3.55 0.000422 ***
## category_code_LT01_8_count -0.16081 0.27259 -0.59 0.555504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.6268
## F-statistic: 167.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.628182907689188
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0062 -0.7902 0.0645 0.9157 3.4484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94479 0.08714 114.130 < 2e-16 ***
## category_code_LT01_1_count 0.39142 0.08292 4.720 3.07e-06 ***
## category_code_LT01_2_count 0.74387 0.07440 9.998 < 2e-16 ***
## category_code_LT01_5_count 0.97601 0.06065 16.092 < 2e-16 ***
## category_code_LT01_7_count 0.51133 0.15162 3.372 0.000804 ***
## category_code_LT01_9_count 0.33599 0.22482 1.495 0.135681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 492 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6282
## F-statistic: 168.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.627465974592533
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7734 0.0593 0.9201 3.4727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92045 0.09051 109.605 < 2e-16 ***
## category_code_LT01_1_count 0.39903 0.08287 4.815 1.96e-06 ***
## category_code_LT01_2_count 0.75050 0.07420 10.114 < 2e-16 ***
## category_code_LT01_5_count 0.98226 0.06051 16.233 < 2e-16 ***
## category_code_LT01_7_count 0.51863 0.15161 3.421 0.000676 ***
## category_code_LT01_10_count 0.12647 0.11168 1.132 0.257995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6275
## F-statistic: 168.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.630690636306077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0164 -0.7473 0.0343 0.8590 3.4354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95777 0.08691 114.569 < 2e-16 ***
## category_code_LT01_1_count 0.35035 0.08498 4.123 4.39e-05 ***
## category_code_LT01_2_count 0.65393 0.08651 7.559 2.01e-13 ***
## category_code_LT01_5_count 0.97201 0.06044 16.083 < 2e-16 ***
## category_code_LT01_7_count 0.43880 0.15565 2.819 0.00501 **
## category_code_LT01_11_count 0.28012 0.11848 2.364 0.01845 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 492 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6307
## F-statistic: 170.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.626494952487153
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7806 0.0413 0.9192 3.4452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.947950 0.087308 113.940 < 2e-16 ***
## category_code_LT01_1_count 0.398352 0.083794 4.754 2.62e-06 ***
## category_code_LT01_2_count 0.763882 0.074769 10.217 < 2e-16 ***
## category_code_LT01_5_count 0.983552 0.060953 16.136 < 2e-16 ***
## category_code_LT01_7_count 0.533971 0.151203 3.531 0.000452 ***
## category_code_LT01_12_count 0.001271 0.206560 0.006 0.995092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6265
## F-statistic: 167.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.626542303277147
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0147 -0.7845 0.0377 0.9110 3.4454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94773 0.08731 113.941 < 2e-16 ***
## category_code_LT01_1_count 0.40143 0.08384 4.788 2.23e-06 ***
## category_code_LT01_2_count 0.76468 0.07339 10.419 < 2e-16 ***
## category_code_LT01_5_count 0.98408 0.06061 16.237 < 2e-16 ***
## category_code_LT01_7_count 0.53816 0.15212 3.538 0.000442 ***
## category_code_LT01_13_count -0.06133 0.24550 -0.250 0.802817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6265
## F-statistic: 167.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.626508262671907
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0145 -0.7755 0.0449 0.8993 3.4446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94856 0.08743 113.790 < 2e-16 ***
## category_code_LT01_1_count 0.39701 0.08366 4.746 2.73e-06 ***
## category_code_LT01_2_count 0.76300 0.07370 10.352 < 2e-16 ***
## category_code_LT01_5_count 0.98262 0.06102 16.104 < 2e-16 ***
## category_code_LT01_7_count 0.53248 0.15162 3.512 0.000486 ***
## category_code_LT01_14_count 0.04331 0.32676 0.133 0.894598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6265
## F-statistic: 167.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.626528059217274
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7678 0.0388 0.9100 3.4456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94753 0.08733 113.913 < 2e-16 ***
## category_code_LT01_1_count 0.40189 0.08462 4.750 2.68e-06 ***
## category_code_LT01_2_count 0.76529 0.07361 10.397 < 2e-16 ***
## category_code_LT01_5_count 0.98362 0.06058 16.238 < 2e-16 ***
## category_code_LT01_7_count 0.53253 0.15135 3.519 0.000474 ***
## category_code_LT01_15_count -0.15825 0.75742 -0.209 0.834590
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6265
## F-statistic: 167.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.626596031615205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7750 0.0435 0.9231 3.4449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94824 0.08730 113.956 < 2e-16 ***
## category_code_LT01_1_count 0.39947 0.08301 4.812 1.99e-06 ***
## category_code_LT01_2_count 0.76031 0.07401 10.272 < 2e-16 ***
## category_code_LT01_5_count 0.98317 0.06058 16.229 < 2e-16 ***
## category_code_LT01_7_count 0.53484 0.15120 3.537 0.000443 ***
## category_code_LT01_16_count 0.42475 1.16371 0.365 0.715273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6266
## F-statistic: 167.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.619808407738866
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0198 -0.7905 0.0124 0.9013 3.4513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94184 0.08814 112.801 < 2e-16 ***
## category_code_LT01_1_count 0.43871 0.08278 5.300 1.76e-07 ***
## category_code_LT01_2_count 0.81703 0.07193 11.358 < 2e-16 ***
## category_code_LT01_5_count 0.99986 0.06182 16.174 < 2e-16 ***
## category_code_LT01_8_count -0.14703 0.27514 -0.534 0.5933
## category_code_LT01_9_count 0.41587 0.22633 1.837 0.0667 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6236, Adjusted R-squared: 0.6198
## F-statistic: 163 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.618805066153215
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9937 -0.7936 0.0107 0.9039 3.4828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91040 0.09152 108.288 < 2e-16 ***
## category_code_LT01_1_count 0.44884 0.08267 5.429 8.91e-08 ***
## category_code_LT01_2_count 0.82595 0.07165 11.528 < 2e-16 ***
## category_code_LT01_5_count 1.00762 0.06168 16.335 < 2e-16 ***
## category_code_LT01_8_count -0.13979 0.27543 -0.508 0.612
## category_code_LT01_10_count 0.16203 0.11255 1.440 0.151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.6188
## F-statistic: 162.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.624851638000497
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0293 -0.7485 0.0394 0.8969 3.4342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95891 0.08763 113.644 < 2e-16 ***
## category_code_LT01_1_count 0.37525 0.08532 4.398 1.34e-05 ***
## category_code_LT01_2_count 0.68352 0.08657 7.895 1.90e-14 ***
## category_code_LT01_5_count 0.98899 0.06153 16.074 < 2e-16 ***
## category_code_LT01_8_count -0.11147 0.27321 -0.408 0.68345
## category_code_LT01_11_count 0.36552 0.11538 3.168 0.00163 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6249
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.617199399466322
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0302 -0.7930 -0.0006 0.9042 3.4478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.945395 0.088419 112.481 < 2e-16 ***
## category_code_LT01_1_count 0.449702 0.083660 5.375 1.18e-07 ***
## category_code_LT01_2_count 0.846135 0.071901 11.768 < 2e-16 ***
## category_code_LT01_5_count 1.009715 0.062132 16.251 < 2e-16 ***
## category_code_LT01_8_count -0.129864 0.276087 -0.470 0.638
## category_code_LT01_12_count 0.001507 0.209240 0.007 0.994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.617208886927589
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7930 0.0002 0.9047 3.4477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94548 0.08842 112.480 < 2e-16 ***
## category_code_LT01_1_count 0.44825 0.08400 5.336 1.45e-07 ***
## category_code_LT01_2_count 0.84564 0.07061 11.977 < 2e-16 ***
## category_code_LT01_5_count 1.00940 0.06188 16.312 < 2e-16 ***
## category_code_LT01_8_count -0.12790 0.27645 -0.463 0.644
## category_code_LT01_13_count 0.02739 0.24751 0.111 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.617320995121488
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7945 0.0019 0.8889 3.4459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94729 0.08853 112.356 < 2e-16 ***
## category_code_LT01_1_count 0.44513 0.08366 5.321 1.57e-07 ***
## category_code_LT01_2_count 0.84262 0.07098 11.872 < 2e-16 ***
## category_code_LT01_5_count 1.00671 0.06227 16.168 < 2e-16 ***
## category_code_LT01_8_count -0.13119 0.27590 -0.476 0.635
## category_code_LT01_14_count 0.13045 0.32987 0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.617301022510657
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0294 -0.7964 0.0102 0.8993 3.4485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94468 0.08843 112.462 < 2e-16 ***
## category_code_LT01_1_count 0.45561 0.08439 5.399 1.04e-07 ***
## category_code_LT01_2_count 0.84815 0.07058 12.016 < 2e-16 ***
## category_code_LT01_5_count 1.00969 0.06179 16.341 < 2e-16 ***
## category_code_LT01_8_count -0.12916 0.27589 -0.468 0.640
## category_code_LT01_15_count -0.27691 0.76594 -0.362 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.617284120799213
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0304 -0.7932 -0.0022 0.9058 3.4475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94570 0.08841 112.493 < 2e-16 ***
## category_code_LT01_1_count 0.45087 0.08290 5.439 8.47e-08 ***
## category_code_LT01_2_count 0.84300 0.07107 11.862 < 2e-16 ***
## category_code_LT01_5_count 1.00957 0.06179 16.338 < 2e-16 ***
## category_code_LT01_8_count -0.13451 0.27626 -0.487 0.627
## category_code_LT01_16_count 0.38937 1.17956 0.330 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.620729091552431
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9845 -0.7879 0.0377 0.9227 3.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91103 0.09127 108.587 < 2e-16 ***
## category_code_LT01_1_count 0.43709 0.08263 5.290 1.84e-07 ***
## category_code_LT01_2_count 0.80256 0.07287 11.013 < 2e-16 ***
## category_code_LT01_5_count 0.99348 0.06101 16.284 < 2e-16 ***
## category_code_LT01_9_count 0.37782 0.22764 1.660 0.0976 .
## category_code_LT01_10_count 0.13760 0.11308 1.217 0.2243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.6207
## F-statistic: 163.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.626774081129305
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0158 -0.7896 0.0652 0.8493 3.4390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95419 0.08740 113.887 < 2e-16 ***
## category_code_LT01_1_count 0.36609 0.08515 4.300 2.06e-05 ***
## category_code_LT01_2_count 0.66243 0.08726 7.592 1.60e-13 ***
## category_code_LT01_5_count 0.97629 0.06081 16.055 < 2e-16 ***
## category_code_LT01_9_count 0.36907 0.22454 1.644 0.1009
## category_code_LT01_11_count 0.35481 0.11528 3.078 0.0022 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.6268
## F-statistic: 167.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.619587828152449
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0150 -0.8033 0.0278 0.9099 3.4526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94052 0.08813 112.796 < 2e-16 ***
## category_code_LT01_1_count 0.43720 0.08357 5.231 2.5e-07 ***
## category_code_LT01_2_count 0.81756 0.07341 11.137 < 2e-16 ***
## category_code_LT01_5_count 0.99482 0.06147 16.185 < 2e-16 ***
## category_code_LT01_9_count 0.41175 0.22626 1.820 0.0694 .
## category_code_LT01_12_count -0.00222 0.20846 -0.011 0.9915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6196
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.619642048059107
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.8088 0.0214 0.9077 3.4524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94075 0.08813 112.802 < 2e-16 ***
## category_code_LT01_1_count 0.43334 0.08393 5.163 3.53e-07 ***
## category_code_LT01_2_count 0.81567 0.07224 11.291 < 2e-16 ***
## category_code_LT01_5_count 0.99395 0.06116 16.251 < 2e-16 ***
## category_code_LT01_9_count 0.41586 0.22678 1.834 0.0673 .
## category_code_LT01_13_count 0.06542 0.24682 0.265 0.7911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6196
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619646152576912
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0148 -0.8094 0.0221 0.9101 3.4513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94187 0.08826 112.647 <2e-16 ***
## category_code_LT01_1_count 0.43394 0.08352 5.195 3e-07 ***
## category_code_LT01_2_count 0.81517 0.07240 11.259 <2e-16 ***
## category_code_LT01_5_count 0.99270 0.06154 16.130 <2e-16 ***
## category_code_LT01_9_count 0.40777 0.22671 1.799 0.0727 .
## category_code_LT01_14_count 0.09057 0.32951 0.275 0.7835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6196
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.619661079051025
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0143 -0.8071 0.0315 0.9066 3.4532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93994 0.08814 112.775 < 2e-16 ***
## category_code_LT01_1_count 0.44209 0.08433 5.242 2.36e-07 ***
## category_code_LT01_2_count 0.81918 0.07218 11.350 < 2e-16 ***
## category_code_LT01_5_count 0.99477 0.06109 16.284 < 2e-16 ***
## category_code_LT01_9_count 0.40954 0.22636 1.809 0.071 .
## category_code_LT01_15_count -0.23530 0.76394 -0.308 0.758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6197
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.619636394384891
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0150 -0.8033 0.0279 0.9169 3.4524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94074 0.08813 112.801 < 2e-16 ***
## category_code_LT01_1_count 0.43790 0.08281 5.288 1.86e-07 ***
## category_code_LT01_2_count 0.81507 0.07254 11.236 < 2e-16 ***
## category_code_LT01_5_count 0.99452 0.06110 16.278 < 2e-16 ***
## category_code_LT01_9_count 0.41002 0.22635 1.811 0.0707 .
## category_code_LT01_16_count 0.29474 1.17490 0.251 0.8020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6196
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.625947557685838
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9934 -0.7766 0.0401 0.8890 3.4662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92696 0.09081 109.312 < 2e-16 ***
## category_code_LT01_1_count 0.37443 0.08512 4.399 1.33e-05 ***
## category_code_LT01_2_count 0.66899 0.08716 7.676 8.93e-14 ***
## category_code_LT01_5_count 0.98324 0.06066 16.208 < 2e-16 ***
## category_code_LT01_10_count 0.14157 0.11162 1.268 0.205
## category_code_LT01_11_count 0.35849 0.11536 3.108 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6297, Adjusted R-squared: 0.6259
## F-statistic: 167.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.618609181186503
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9895 -0.7931 0.0247 0.9006 3.4838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90932 0.09153 108.258 < 2e-16 ***
## category_code_LT01_1_count 0.44800 0.08346 5.368 1.23e-07 ***
## category_code_LT01_2_count 0.82718 0.07302 11.328 < 2e-16 ***
## category_code_LT01_5_count 1.00314 0.06130 16.365 < 2e-16 ***
## category_code_LT01_10_count 0.16091 0.11264 1.429 0.154
## category_code_LT01_12_count -0.01443 0.20891 -0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6186
## F-statistic: 162.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.618612225911031
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9893 -0.7914 0.0226 0.8961 3.4836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90959 0.09154 108.255 < 2e-16 ***
## category_code_LT01_1_count 0.44592 0.08375 5.325 1.54e-07 ***
## category_code_LT01_2_count 0.82574 0.07184 11.495 < 2e-16 ***
## category_code_LT01_5_count 1.00244 0.06098 16.438 < 2e-16 ***
## category_code_LT01_10_count 0.16024 0.11260 1.423 0.155
## category_code_LT01_13_count 0.02301 0.24671 0.093 0.926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6186
## F-statistic: 162.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.61861037127074
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7951 0.0227 0.8984 3.4829
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91025 0.09211 107.596 < 2e-16 ***
## category_code_LT01_1_count 0.44624 0.08349 5.345 1.38e-07 ***
## category_code_LT01_2_count 0.82572 0.07193 11.479 < 2e-16 ***
## category_code_LT01_5_count 1.00207 0.06140 16.319 < 2e-16 ***
## category_code_LT01_10_count 0.15858 0.11534 1.375 0.170
## category_code_LT01_14_count 0.02680 0.33748 0.079 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6186
## F-statistic: 162.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.618788123562164
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9873 -0.7893 0.0359 0.8936 3.4857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90749 0.09159 108.175 < 2e-16 ***
## category_code_LT01_1_count 0.45500 0.08416 5.406 1.01e-07 ***
## category_code_LT01_2_count 0.82821 0.07177 11.540 < 2e-16 ***
## category_code_LT01_5_count 1.00255 0.06091 16.458 < 2e-16 ***
## category_code_LT01_10_count 0.16514 0.11291 1.463 0.144
## category_code_LT01_15_count -0.37243 0.76709 -0.486 0.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.6188
## F-statistic: 162.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.618655718572225
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9896 -0.7917 0.0239 0.8969 3.4833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90985 0.09153 108.267 < 2e-16 ***
## category_code_LT01_1_count 0.44799 0.08269 5.418 9.45e-08 ***
## category_code_LT01_2_count 0.82385 0.07226 11.402 < 2e-16 ***
## category_code_LT01_5_count 1.00242 0.06093 16.451 < 2e-16 ***
## category_code_LT01_10_count 0.15955 0.11261 1.417 0.157
## category_code_LT01_16_count 0.29955 1.17663 0.255 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6187
## F-statistic: 162.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.625140493602474
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.7816 0.0403 0.8678 3.4348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95832 0.08757 113.722 < 2e-16 ***
## category_code_LT01_1_count 0.37857 0.08546 4.430 1.16e-05 ***
## category_code_LT01_2_count 0.68540 0.08659 7.916 1.64e-14 ***
## category_code_LT01_5_count 0.98900 0.06096 16.225 < 2e-16 ***
## category_code_LT01_11_count 0.38606 0.11831 3.263 0.00118 **
## category_code_LT01_12_count -0.15684 0.21231 -0.739 0.46043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6251
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.624725581966173
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7620 0.0455 0.8838 3.4352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.957947 0.087618 113.652 < 2e-16 ***
## category_code_LT01_1_count 0.373290 0.086201 4.330 1.8e-05 ***
## category_code_LT01_2_count 0.683013 0.086660 7.882 2.1e-14 ***
## category_code_LT01_5_count 0.984897 0.060793 16.201 < 2e-16 ***
## category_code_LT01_11_count 0.366382 0.115440 3.174 0.0016 **
## category_code_LT01_13_count 0.008291 0.244732 0.034 0.9730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6247
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.624810157122276
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0253 -0.7736 0.0432 0.8881 3.4337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95947 0.08773 113.528 < 2e-16 ***
## category_code_LT01_1_count 0.36995 0.08598 4.303 2.04e-05 ***
## category_code_LT01_2_count 0.68042 0.08695 7.825 3.12e-14 ***
## category_code_LT01_5_count 0.98242 0.06122 16.048 < 2e-16 ***
## category_code_LT01_11_count 0.36580 0.11538 3.170 0.00162 **
## category_code_LT01_14_count 0.10934 0.32665 0.335 0.73796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6248
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.624868041267123
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0247 -0.7467 0.0450 0.8700 3.4360
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95712 0.08762 113.644 < 2e-16 ***
## category_code_LT01_1_count 0.38044 0.08663 4.391 1.38e-05 ***
## category_code_LT01_2_count 0.68495 0.08667 7.903 1.80e-14 ***
## category_code_LT01_5_count 0.98487 0.06073 16.216 < 2e-16 ***
## category_code_LT01_11_count 0.36754 0.11538 3.186 0.00154 **
## category_code_LT01_15_count -0.32886 0.75847 -0.434 0.66479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6249
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.624810183534469
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7607 0.0476 0.8891 3.4350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95819 0.08761 113.668 < 2e-16 ***
## category_code_LT01_1_count 0.37469 0.08529 4.393 1.37e-05 ***
## category_code_LT01_2_count 0.67975 0.08716 7.799 3.77e-14 ***
## category_code_LT01_5_count 0.98460 0.06075 16.208 < 2e-16 ***
## category_code_LT01_11_count 0.36684 0.11536 3.180 0.00157 **
## category_code_LT01_16_count 0.39050 1.16638 0.335 0.73792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6248
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617042443932189
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0257 -0.7982 0.0045 0.8853 3.4488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.944332 0.088405 112.49 < 2e-16 ***
## category_code_LT01_1_count 0.446454 0.084722 5.27 2.05e-07 ***
## category_code_LT01_2_count 0.845701 0.072110 11.73 < 2e-16 ***
## category_code_LT01_5_count 1.004861 0.061460 16.35 < 2e-16 ***
## category_code_LT01_12_count -0.002177 0.209163 -0.01 0.992
## category_code_LT01_13_count 0.034517 0.247093 0.14 0.889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.617
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617145996800669
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0257 -0.7943 0.0059 0.8886 3.4471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.946052 0.088516 112.365 < 2e-16 ***
## category_code_LT01_1_count 0.444012 0.084351 5.264 2.11e-07 ***
## category_code_LT01_2_count 0.843218 0.072376 11.650 < 2e-16 ***
## category_code_LT01_5_count 1.002313 0.061847 16.206 < 2e-16 ***
## category_code_LT01_12_count -0.006979 0.209527 -0.033 0.973
## category_code_LT01_14_count 0.129121 0.330545 0.391 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617131032622384
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0252 -0.8003 0.0161 0.8793 3.4497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.943472 0.088412 112.468 < 2e-16 ***
## category_code_LT01_1_count 0.454432 0.085261 5.330 1.5e-07 ***
## category_code_LT01_2_count 0.848611 0.072148 11.762 < 2e-16 ***
## category_code_LT01_5_count 1.005280 0.061399 16.373 < 2e-16 ***
## category_code_LT01_12_count -0.005221 0.209326 -0.025 0.980
## category_code_LT01_15_count -0.280024 0.766808 -0.365 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617099721354573
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0258 -0.7976 0.0064 0.8896 3.4487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.944438 0.088397 112.498 < 2e-16 ***
## category_code_LT01_1_count 0.449250 0.083674 5.369 1.22e-07 ***
## category_code_LT01_2_count 0.843394 0.072599 11.617 < 2e-16 ***
## category_code_LT01_5_count 1.004863 0.061412 16.363 < 2e-16 ***
## category_code_LT01_12_count -0.001013 0.209161 -0.005 0.996
## category_code_LT01_16_count 0.359579 1.178383 0.305 0.760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617161430747486
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.7938 0.0069 0.8908 3.4469
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94621 0.08852 112.361 < 2e-16 ***
## category_code_LT01_1_count 0.44164 0.08476 5.211 2.77e-07 ***
## category_code_LT01_2_count 0.84194 0.07121 11.823 < 2e-16 ***
## category_code_LT01_5_count 1.00171 0.06158 16.266 < 2e-16 ***
## category_code_LT01_13_count 0.03576 0.24707 0.145 0.885
## category_code_LT01_14_count 0.12906 0.32994 0.391 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6172
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.617139499915256
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0250 -0.7987 0.0124 0.8811 3.4495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94361 0.08842 112.463 < 2e-16 ***
## category_code_LT01_1_count 0.45249 0.08570 5.280 1.94e-07 ***
## category_code_LT01_2_count 0.84760 0.07085 11.963 < 2e-16 ***
## category_code_LT01_5_count 1.00483 0.06108 16.452 < 2e-16 ***
## category_code_LT01_13_count 0.02661 0.24806 0.107 0.915
## category_code_LT01_15_count -0.27177 0.76921 -0.353 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617117445561659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0257 -0.7971 0.0063 0.8921 3.4486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94460 0.08840 112.496 < 2e-16 ***
## category_code_LT01_1_count 0.44715 0.08395 5.326 1.53e-07 ***
## category_code_LT01_2_count 0.84244 0.07132 11.812 < 2e-16 ***
## category_code_LT01_5_count 1.00443 0.06109 16.442 < 2e-16 ***
## category_code_LT01_13_count 0.03733 0.24723 0.151 0.880
## category_code_LT01_16_count 0.36623 1.17904 0.311 0.756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.617246943035735
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0248 -0.7933 0.0215 0.8862 3.4478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94534 0.08853 112.343 < 2e-16 ***
## category_code_LT01_1_count 0.44951 0.08517 5.278 1.96e-07 ***
## category_code_LT01_2_count 0.84469 0.07118 11.866 < 2e-16 ***
## category_code_LT01_5_count 1.00208 0.06151 16.290 < 2e-16 ***
## category_code_LT01_14_count 0.12760 0.32988 0.387 0.699
## category_code_LT01_15_count -0.27711 0.76600 -0.362 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617228291457265
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7939 0.0076 0.8961 3.4468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94640 0.08851 112.373 < 2e-16 ***
## category_code_LT01_1_count 0.44445 0.08365 5.313 1.64e-07 ***
## category_code_LT01_2_count 0.83938 0.07173 11.702 < 2e-16 ***
## category_code_LT01_5_count 1.00161 0.06153 16.277 < 2e-16 ***
## category_code_LT01_14_count 0.13431 0.33037 0.407 0.685
## category_code_LT01_16_count 0.38572 1.17981 0.327 0.744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617197791479482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0251 -0.7982 0.0174 0.8853 3.4494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94374 0.08841 112.477 < 2e-16 ***
## category_code_LT01_1_count 0.45489 0.08438 5.391 1.09e-07 ***
## category_code_LT01_2_count 0.84531 0.07129 11.857 < 2e-16 ***
## category_code_LT01_5_count 1.00480 0.06103 16.465 < 2e-16 ***
## category_code_LT01_15_count -0.27212 0.76641 -0.355 0.723
## category_code_LT01_16_count 0.34651 1.17869 0.294 0.769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.629891446524928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.7402 0.0587 0.9013 3.4736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95065 0.08685 114.572 < 2e-16 ***
## category_code_LT01_1_count 0.25318 0.08851 2.860 0.00441 **
## category_code_LT01_3_count 0.30617 0.11271 2.716 0.00683 **
## category_code_LT01_4_count 0.73809 0.09231 7.996 9.27e-15 ***
## category_code_LT01_5_count 0.91957 0.06179 14.882 < 2e-16 ***
## category_code_LT01_6_count 0.42197 0.14957 2.821 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 492 degrees of freedom
## Multiple R-squared: 0.6336, Adjusted R-squared: 0.6299
## F-statistic: 170.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.630739361166809
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9949 -0.7507 0.0240 0.8594 3.4485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96709 0.08660 115.094 < 2e-16 ***
## category_code_LT01_1_count 0.24792 0.08852 2.801 0.00530 **
## category_code_LT01_3_count 0.32589 0.11203 2.909 0.00379 **
## category_code_LT01_4_count 0.72989 0.09251 7.890 1.97e-14 ***
## category_code_LT01_5_count 0.92751 0.06145 15.094 < 2e-16 ***
## category_code_LT01_7_count 0.46012 0.15247 3.018 0.00268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 492 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.6307
## F-statistic: 170.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.624212318992387
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0067 -0.7670 0.0229 0.8452 3.4295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96672 0.08739 114.047 < 2e-16 ***
## category_code_LT01_1_count 0.27362 0.08898 3.075 0.00222 **
## category_code_LT01_3_count 0.34166 0.11293 3.025 0.00261 **
## category_code_LT01_4_count 0.81191 0.08919 9.103 < 2e-16 ***
## category_code_LT01_5_count 0.94517 0.06258 15.103 < 2e-16 ***
## category_code_LT01_8_count -0.17369 0.27343 -0.635 0.52558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6242
## F-statistic: 166.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.626366236949272
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9914 -0.7507 0.0435 0.8920 3.4506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96115 0.08714 114.314 < 2e-16 ***
## category_code_LT01_1_count 0.26591 0.08875 2.996 0.00287 **
## category_code_LT01_3_count 0.30902 0.11393 2.712 0.00691 **
## category_code_LT01_4_count 0.79728 0.08931 8.927 < 2e-16 ***
## category_code_LT01_5_count 0.93102 0.06185 15.052 < 2e-16 ***
## category_code_LT01_9_count 0.40565 0.22529 1.801 0.07238 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6264
## F-statistic: 167.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.624786205996205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9748 -0.7687 0.0064 0.8803 3.3300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93898 0.09064 109.654 < 2e-16 ***
## category_code_LT01_1_count 0.27535 0.08893 3.096 0.00207 **
## category_code_LT01_3_count 0.31703 0.11490 2.759 0.00601 **
## category_code_LT01_4_count 0.80542 0.08933 9.016 < 2e-16 ***
## category_code_LT01_5_count 0.93909 0.06182 15.191 < 2e-16 ***
## category_code_LT01_10_count 0.12189 0.11334 1.075 0.28269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6248
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.630985237117937
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0037 -0.7636 0.0431 0.8734 3.4385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97401 0.08662 115.152 < 2e-16 ***
## category_code_LT01_1_count 0.22776 0.08930 2.551 0.01106 *
## category_code_LT01_3_count 0.25395 0.11537 2.201 0.02819 *
## category_code_LT01_4_count 0.68454 0.09764 7.011 7.85e-12 ***
## category_code_LT01_5_count 0.93026 0.06138 15.157 < 2e-16 ***
## category_code_LT01_11_count 0.34945 0.11373 3.073 0.00224 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 492 degrees of freedom
## Multiple R-squared: 0.6347, Adjusted R-squared: 0.631
## F-statistic: 171 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.624000380003177
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7547 0.0174 0.8662 3.4401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96514 0.08738 114.038 < 2e-16 ***
## category_code_LT01_1_count 0.26814 0.08961 2.992 0.00291 **
## category_code_LT01_3_count 0.33766 0.11321 2.983 0.00300 **
## category_code_LT01_4_count 0.80872 0.08971 9.015 < 2e-16 ***
## category_code_LT01_5_count 0.93691 0.06221 15.060 < 2e-16 ***
## category_code_LT01_12_count 0.07309 0.20595 0.355 0.72281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.624
## F-statistic: 166 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.623910557733028
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0010 -0.7581 0.0347 0.8452 3.4377
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96530 0.08740 114.022 < 2e-16 ***
## category_code_LT01_1_count 0.27082 0.08984 3.014 0.00271 **
## category_code_LT01_3_count 0.34040 0.11296 3.014 0.00271 **
## category_code_LT01_4_count 0.81148 0.08944 9.072 < 2e-16 ***
## category_code_LT01_5_count 0.93897 0.06193 15.162 < 2e-16 ***
## category_code_LT01_13_count 0.02248 0.24493 0.092 0.92690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6239
## F-statistic: 165.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.623954261377989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7579 0.0251 0.8602 3.4384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96634 0.08750 113.904 < 2e-16 ***
## category_code_LT01_1_count 0.26948 0.08949 3.011 0.00274 **
## category_code_LT01_3_count 0.34199 0.11312 3.023 0.00263 **
## category_code_LT01_4_count 0.80834 0.09039 8.943 < 2e-16 ***
## category_code_LT01_5_count 0.93734 0.06230 15.046 < 2e-16 ***
## category_code_LT01_14_count 0.08417 0.32862 0.256 0.79795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.624
## F-statistic: 165.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.624106850441426
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9997 -0.7569 0.0258 0.8470 3.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96415 0.08740 114.010 < 2e-16 ***
## category_code_LT01_1_count 0.27949 0.09014 3.100 0.00204 **
## category_code_LT01_3_count 0.34684 0.11361 3.053 0.00239 **
## category_code_LT01_4_count 0.81198 0.08920 9.103 < 2e-16 ***
## category_code_LT01_5_count 0.93876 0.06188 15.171 < 2e-16 ***
## category_code_LT01_15_count -0.39299 0.76291 -0.515 0.60670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6241
## F-statistic: 166 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.624344649783696
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0008 -0.7558 0.0227 0.8549 3.4381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96551 0.08734 114.094 < 2e-16 ***
## category_code_LT01_1_count 0.27407 0.08897 3.081 0.00218 **
## category_code_LT01_3_count 0.32929 0.11384 2.893 0.00399 **
## category_code_LT01_4_count 0.81223 0.08917 9.109 < 2e-16 ***
## category_code_LT01_5_count 0.93837 0.06186 15.168 < 2e-16 ***
## category_code_LT01_16_count 0.88752 1.16842 0.760 0.44787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6243
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.601565597065753
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0366 -0.8248 0.0248 0.9233 3.6653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97677 0.09004 110.803 < 2e-16 ***
## category_code_LT01_1_count 0.43956 0.08650 5.082 5.33e-07 ***
## category_code_LT01_3_count 0.60465 0.10697 5.652 2.68e-08 ***
## category_code_LT01_5_count 0.97371 0.06345 15.345 < 2e-16 ***
## category_code_LT01_6_count 0.69550 0.14940 4.655 4.17e-06 ***
## category_code_LT01_7_count 0.75080 0.15197 4.940 1.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 492 degrees of freedom
## Multiple R-squared: 0.6056, Adjusted R-squared: 0.6016
## F-statistic: 151.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.604527218368211
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0516 -0.7909 0.0255 0.9265 3.4029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99024 0.08973 111.335 < 2e-16 ***
## category_code_LT01_1_count 0.38760 0.08836 4.387 1.41e-05 ***
## category_code_LT01_3_count 0.46101 0.11401 4.044 6.11e-05 ***
## category_code_LT01_5_count 0.97600 0.06312 15.462 < 2e-16 ***
## category_code_LT01_6_count 0.55200 0.15340 3.599 0.000352 ***
## category_code_LT01_11_count 0.58639 0.11028 5.317 1.60e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 492 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6045
## F-statistic: 152.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.603593063920546
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0823 -0.8009 0.1123 0.8666 3.3818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01134 0.08957 111.768 < 2e-16 ***
## category_code_LT01_1_count 0.39083 0.08841 4.421 1.21e-05 ***
## category_code_LT01_3_count 0.50094 0.11316 4.427 1.18e-05 ***
## category_code_LT01_5_count 0.98981 0.06279 15.764 < 2e-16 ***
## category_code_LT01_7_count 0.54949 0.16024 3.429 0.000656 ***
## category_code_LT01_11_count 0.55824 0.11325 4.929 1.13e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 492 degrees of freedom
## Multiple R-squared: 0.6076, Adjusted R-squared: 0.6036
## F-statistic: 152.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.631604353728519
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9860 -0.7521 0.0489 0.8882 3.9004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96253 0.08657 115.084 < 2e-16 ***
## category_code_LT01_1_count 0.25817 0.08797 2.935 0.00349 **
## category_code_LT01_4_count 0.75801 0.08772 8.641 < 2e-16 ***
## category_code_LT01_5_count 0.92133 0.06155 14.968 < 2e-16 ***
## category_code_LT01_6_count 0.46058 0.14837 3.104 0.00202 **
## category_code_LT01_7_count 0.47391 0.15216 3.115 0.00195 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 492 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6316
## F-statistic: 171.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.62471107865538
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9992 -0.7673 0.0430 0.9457 3.8810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96256 0.08740 113.988 < 2e-16 ***
## category_code_LT01_1_count 0.28605 0.08842 3.235 0.00130 **
## category_code_LT01_4_count 0.84693 0.08367 10.122 < 2e-16 ***
## category_code_LT01_5_count 0.94035 0.06268 15.002 < 2e-16 ***
## category_code_LT01_6_count 0.46954 0.14984 3.134 0.00183 **
## category_code_LT01_8_count -0.19054 0.27339 -0.697 0.48616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6247
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.627482610292706
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9812 -0.7994 0.0525 0.9245 3.8956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95585 0.08709 114.323 < 2e-16 ***
## category_code_LT01_1_count 0.27479 0.08818 3.116 0.00194 **
## category_code_LT01_4_count 0.82154 0.08432 9.744 < 2e-16 ***
## category_code_LT01_5_count 0.92393 0.06196 14.912 < 2e-16 ***
## category_code_LT01_6_count 0.44493 0.14953 2.975 0.00307 **
## category_code_LT01_9_count 0.45391 0.22282 2.037 0.04217 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6275
## F-statistic: 168.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.625282201236564
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9664 -0.7728 0.0580 0.9588 3.7711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93413 0.09059 109.660 < 2e-16 ***
## category_code_LT01_1_count 0.28669 0.08834 3.245 0.00125 **
## category_code_LT01_4_count 0.83718 0.08410 9.954 < 2e-16 ***
## category_code_LT01_5_count 0.93397 0.06195 15.077 < 2e-16 ***
## category_code_LT01_6_count 0.43683 0.15186 2.876 0.00420 **
## category_code_LT01_10_count 0.12551 0.11288 1.112 0.26672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6253
## F-statistic: 166.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.632096932008251
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9954 -0.7590 0.0698 0.8826 3.6603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96982 0.08655 115.190 < 2e-16 ***
## category_code_LT01_1_count 0.23356 0.08891 2.627 0.00889 **
## category_code_LT01_4_count 0.69919 0.09476 7.379 6.85e-13 ***
## category_code_LT01_5_count 0.92426 0.06145 15.040 < 2e-16 ***
## category_code_LT01_6_count 0.37952 0.15065 2.519 0.01208 *
## category_code_LT01_11_count 0.36043 0.11191 3.221 0.00136 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 492 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6321
## F-statistic: 171.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.624368139545266
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9925 -0.7652 0.0530 0.9470 3.8852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96103 0.08741 113.955 < 2e-16 ***
## category_code_LT01_1_count 0.28226 0.08907 3.169 0.00162 **
## category_code_LT01_4_count 0.84569 0.08416 10.049 < 2e-16 ***
## category_code_LT01_5_count 0.93277 0.06230 14.972 < 2e-16 ***
## category_code_LT01_6_count 0.46229 0.15089 3.064 0.00231 **
## category_code_LT01_12_count 0.03931 0.20685 0.190 0.84936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6244
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.624360831429201
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9931 -0.7662 0.0463 0.9448 3.8851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96112 0.08742 113.951 < 2e-16 ***
## category_code_LT01_1_count 0.28223 0.08932 3.160 0.00168 **
## category_code_LT01_4_count 0.84621 0.08400 10.074 < 2e-16 ***
## category_code_LT01_5_count 0.93350 0.06207 15.040 < 2e-16 ***
## category_code_LT01_6_count 0.46626 0.14984 3.112 0.00197 **
## category_code_LT01_13_count 0.03989 0.24485 0.163 0.87065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6244
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.624459614476696
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9927 -0.7621 0.0540 0.9487 3.8849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96261 0.08750 113.864 < 2e-16 ***
## category_code_LT01_1_count 0.28042 0.08895 3.152 0.00172 **
## category_code_LT01_4_count 0.84136 0.08506 9.892 < 2e-16 ***
## category_code_LT01_5_count 0.93090 0.06248 14.900 < 2e-16 ***
## category_code_LT01_6_count 0.47149 0.15050 3.133 0.00183 **
## category_code_LT01_14_count 0.13011 0.32946 0.395 0.69307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6245
## F-statistic: 166.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.62441129147113
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9926 -0.7581 0.0522 0.9417 3.8811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96045 0.08743 113.930 < 2e-16 ***
## category_code_LT01_1_count 0.28903 0.08978 3.219 0.00137 **
## category_code_LT01_4_count 0.84839 0.08377 10.128 < 2e-16 ***
## category_code_LT01_5_count 0.93376 0.06202 15.056 < 2e-16 ***
## category_code_LT01_6_count 0.46753 0.14992 3.119 0.00192 **
## category_code_LT01_15_count -0.23090 0.75858 -0.304 0.76097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6244
## F-statistic: 166.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.62569131689879
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9904 -0.7601 0.0532 0.9583 3.8839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96030 0.08726 114.146 < 2e-16 ***
## category_code_LT01_1_count 0.28520 0.08827 3.231 0.00132 **
## category_code_LT01_4_count 0.83770 0.08387 9.988 < 2e-16 ***
## category_code_LT01_5_count 0.93084 0.06195 15.025 < 2e-16 ***
## category_code_LT01_6_count 0.47769 0.14981 3.189 0.00152 **
## category_code_LT01_16_count 1.54394 1.15871 1.332 0.18332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.6257
## F-statistic: 167.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.624735239148689
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0256 -0.7711 0.0106 0.8776 3.8633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98157 0.08722 114.439 <2e-16 ***
## category_code_LT01_1_count 0.28420 0.08847 3.212 0.0014 **
## category_code_LT01_4_count 0.85196 0.08307 10.256 <2e-16 ***
## category_code_LT01_5_count 0.95096 0.06238 15.246 <2e-16 ***
## category_code_LT01_7_count 0.48220 0.15363 3.139 0.0018 **
## category_code_LT01_8_count -0.18433 0.27331 -0.674 0.5003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6247
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.627199518178657
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0072 -0.7645 0.0300 0.8494 3.8777
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97420 0.08695 114.710 < 2e-16 ***
## category_code_LT01_1_count 0.27413 0.08825 3.106 0.00200 **
## category_code_LT01_4_count 0.82955 0.08363 9.920 < 2e-16 ***
## category_code_LT01_5_count 0.93499 0.06166 15.163 < 2e-16 ***
## category_code_LT01_7_count 0.44800 0.15391 2.911 0.00377 **
## category_code_LT01_9_count 0.43073 0.22362 1.926 0.05466 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6272
## F-statistic: 168.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.625789321538821
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9849 -0.7648 0.0290 0.8643 3.7322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94613 0.09056 109.834 < 2e-16 ***
## category_code_LT01_1_count 0.28453 0.08832 3.222 0.00136 **
## category_code_LT01_4_count 0.83625 0.08378 9.982 < 2e-16 ***
## category_code_LT01_5_count 0.94343 0.06158 15.320 < 2e-16 ***
## category_code_LT01_7_count 0.46062 0.15395 2.992 0.00291 **
## category_code_LT01_10_count 0.15144 0.11158 1.357 0.17534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6258
## F-statistic: 167.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.631329069113283
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0174 -0.7745 0.0318 0.8497 3.6535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98494 0.08643 115.521 < 2e-16 ***
## category_code_LT01_1_count 0.23587 0.08898 2.651 0.00829 **
## category_code_LT01_4_count 0.71518 0.09384 7.621 1.3e-13 ***
## category_code_LT01_5_count 0.93433 0.06121 15.264 < 2e-16 ***
## category_code_LT01_7_count 0.36176 0.15702 2.304 0.02164 *
## category_code_LT01_11_count 0.34614 0.11373 3.043 0.00246 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 492 degrees of freedom
## Multiple R-squared: 0.635, Adjusted R-squared: 0.6313
## F-statistic: 171.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.624613399162834
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0167 -0.7692 0.0144 0.8690 3.8708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97963 0.08720 114.441 < 2e-16 ***
## category_code_LT01_1_count 0.27621 0.08919 3.097 0.00207 **
## category_code_LT01_4_count 0.84555 0.08397 10.069 < 2e-16 ***
## category_code_LT01_5_count 0.94095 0.06204 15.167 < 2e-16 ***
## category_code_LT01_7_count 0.47870 0.15359 3.117 0.00194 **
## category_code_LT01_12_count 0.11151 0.20530 0.543 0.58725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6284, Adjusted R-squared: 0.6246
## F-statistic: 166.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.624433038002255
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0197 -0.7693 0.0146 0.8685 3.8644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97974 0.08723 114.413 < 2e-16 ***
## category_code_LT01_1_count 0.28527 0.08923 3.197 0.00148 **
## category_code_LT01_4_count 0.85295 0.08316 10.256 < 2e-16 ***
## category_code_LT01_5_count 0.94504 0.06171 15.314 < 2e-16 ***
## category_code_LT01_7_count 0.48319 0.15452 3.127 0.00187 **
## category_code_LT01_13_count -0.05961 0.24619 -0.242 0.80879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6244
## F-statistic: 166.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.624395818392793
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.7690 0.0149 0.8687 3.8660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97947 0.08735 114.251 < 2e-16 ***
## category_code_LT01_1_count 0.28330 0.08888 3.188 0.00153 **
## category_code_LT01_4_count 0.85320 0.08374 10.188 < 2e-16 ***
## category_code_LT01_5_count 0.94526 0.06204 15.237 < 2e-16 ***
## category_code_LT01_7_count 0.48010 0.15392 3.119 0.00192 **
## category_code_LT01_14_count -0.03263 0.32856 -0.099 0.92093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6244
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.624389995276741
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0195 -0.7694 0.0168 0.8686 3.8657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97985 0.08724 114.392 < 2e-16 ***
## category_code_LT01_1_count 0.28323 0.08995 3.149 0.00174 **
## category_code_LT01_4_count 0.85246 0.08332 10.231 < 2e-16 ***
## category_code_LT01_5_count 0.94461 0.06169 15.312 < 2e-16 ***
## category_code_LT01_7_count 0.47886 0.15377 3.114 0.00195 **
## category_code_LT01_15_count -0.03586 0.75871 -0.047 0.96232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6244
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.625356346488493
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7693 0.0207 0.8617 3.8654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97971 0.08712 114.557 < 2e-16 ***
## category_code_LT01_1_count 0.28382 0.08837 3.212 0.00141 **
## category_code_LT01_4_count 0.84627 0.08317 10.176 < 2e-16 ***
## category_code_LT01_5_count 0.94257 0.06164 15.292 < 2e-16 ***
## category_code_LT01_7_count 0.47832 0.15343 3.117 0.00193 **
## category_code_LT01_16_count 1.30472 1.15716 1.128 0.26007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6254
## F-statistic: 166.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.621106589830821
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0181 -0.7890 0.0245 0.9079 3.8609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97358 0.08769 113.740 < 2e-16 ***
## category_code_LT01_1_count 0.29922 0.08864 3.376 0.000794 ***
## category_code_LT01_4_count 0.91051 0.07951 11.451 < 2e-16 ***
## category_code_LT01_5_count 0.95159 0.06277 15.161 < 2e-16 ***
## category_code_LT01_8_count -0.17907 0.27465 -0.652 0.514721
## category_code_LT01_9_count 0.50386 0.22431 2.246 0.025130 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 492 degrees of freedom
## Multiple R-squared: 0.6249, Adjusted R-squared: 0.6211
## F-statistic: 163.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.619274995890525
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9907 -0.7709 0.0301 0.9008 3.6854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93938 0.09132 108.840 < 2e-16 ***
## category_code_LT01_1_count 0.31215 0.08871 3.519 0.000473 ***
## category_code_LT01_4_count 0.92043 0.07961 11.561 < 2e-16 ***
## category_code_LT01_5_count 0.96147 0.06271 15.331 < 2e-16 ***
## category_code_LT01_8_count -0.16980 0.27525 -0.617 0.537591
## category_code_LT01_10_count 0.18270 0.11213 1.629 0.103898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6231, Adjusted R-squared: 0.6193
## F-statistic: 162.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.62753140871905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7663 0.0256 0.9003 3.6005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98608 0.08691 114.901 < 2e-16 ***
## category_code_LT01_1_count 0.24772 0.08938 2.771 0.005792 **
## category_code_LT01_4_count 0.75546 0.09276 8.144 3.17e-15 ***
## category_code_LT01_5_count 0.94630 0.06219 15.216 < 2e-16 ***
## category_code_LT01_8_count -0.13277 0.27227 -0.488 0.626014
## category_code_LT01_11_count 0.40909 0.11085 3.690 0.000249 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6275
## F-statistic: 168.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.617477817600077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0296 -0.7693 0.0100 0.9288 3.8513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97973 0.08806 113.328 < 2e-16 ***
## category_code_LT01_1_count 0.30414 0.08966 3.392 0.00075 ***
## category_code_LT01_4_count 0.93673 0.07947 11.788 < 2e-16 ***
## category_code_LT01_5_count 0.95939 0.06319 15.182 < 2e-16 ***
## category_code_LT01_8_count -0.16452 0.27597 -0.596 0.55135
## category_code_LT01_12_count 0.11920 0.20735 0.575 0.56563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.617223446786598
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0324 -0.7559 -0.0057 0.9169 3.8469
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98003 0.08809 113.292 < 2e-16 ***
## category_code_LT01_1_count 0.31006 0.08985 3.451 0.000607 ***
## category_code_LT01_4_count 0.94352 0.07877 11.978 < 2e-16 ***
## category_code_LT01_5_count 0.96297 0.06294 15.300 < 2e-16 ***
## category_code_LT01_8_count -0.15828 0.27646 -0.573 0.567236
## category_code_LT01_13_count 0.01426 0.24759 0.058 0.954101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.617228254957208
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0325 -0.7620 -0.0056 0.9255 3.8466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98045 0.08821 113.142 < 2e-16 ***
## category_code_LT01_1_count 0.30991 0.08940 3.467 0.000573 ***
## category_code_LT01_4_count 0.94271 0.07945 11.865 < 2e-16 ***
## category_code_LT01_5_count 0.96247 0.06324 15.220 < 2e-16 ***
## category_code_LT01_8_count -0.15959 0.27593 -0.578 0.563290
## category_code_LT01_14_count 0.03226 0.33108 0.097 0.922409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.617244994267627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0322 -0.7589 0.0018 0.9257 3.8448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97971 0.08810 113.275 < 2e-16 ***
## category_code_LT01_1_count 0.31361 0.09037 3.470 0.000565 ***
## category_code_LT01_4_count 0.94472 0.07865 12.012 < 2e-16 ***
## category_code_LT01_5_count 0.96310 0.06287 15.318 < 2e-16 ***
## category_code_LT01_8_count -0.15899 0.27591 -0.576 0.564711
## category_code_LT01_15_count -0.13476 0.76520 -0.176 0.860282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.618270091515328
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0313 -0.7628 0.0005 0.9268 3.8455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97991 0.08797 113.449 < 2e-16 ***
## category_code_LT01_1_count 0.31233 0.08883 3.516 0.000478 ***
## category_code_LT01_4_count 0.93760 0.07859 11.931 < 2e-16 ***
## category_code_LT01_5_count 0.96154 0.06280 15.310 < 2e-16 ***
## category_code_LT01_8_count -0.17551 0.27589 -0.636 0.524968
## category_code_LT01_16_count 1.36004 1.16954 1.163 0.245438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6183
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.622159429135051
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9786 -0.7706 0.0154 0.8955 3.7295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93892 0.09096 109.271 < 2e-16 ***
## category_code_LT01_1_count 0.29944 0.08849 3.384 0.000771 ***
## category_code_LT01_4_count 0.89370 0.08038 11.118 < 2e-16 ***
## category_code_LT01_5_count 0.94474 0.06197 15.244 < 2e-16 ***
## category_code_LT01_9_count 0.45947 0.22583 2.035 0.042432 *
## category_code_LT01_10_count 0.15102 0.11265 1.341 0.180670
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6222
## F-statistic: 164.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.630107747273319
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0100 -0.7686 0.0476 0.9134 3.6262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97904 0.08663 115.190 < 2e-16 ***
## category_code_LT01_1_count 0.23922 0.08909 2.685 0.007494 **
## category_code_LT01_4_count 0.73542 0.09296 7.911 1.7e-14 ***
## category_code_LT01_5_count 0.93204 0.06143 15.171 < 2e-16 ***
## category_code_LT01_9_count 0.42601 0.22249 1.915 0.056103 .
## category_code_LT01_11_count 0.39069 0.11091 3.523 0.000467 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 492 degrees of freedom
## Multiple R-squared: 0.6338, Adjusted R-squared: 0.6301
## F-statistic: 170.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.620988159390923
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0096 -0.7788 0.0349 0.9269 3.8681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97179 0.08767 113.741 < 2e-16 ***
## category_code_LT01_1_count 0.29148 0.08936 3.262 0.00118 **
## category_code_LT01_4_count 0.90414 0.08045 11.238 < 2e-16 ***
## category_code_LT01_5_count 0.94196 0.06243 15.089 < 2e-16 ***
## category_code_LT01_9_count 0.49721 0.22426 2.217 0.02707 *
## category_code_LT01_12_count 0.10744 0.20632 0.521 0.60276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.621
## F-statistic: 163.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.620824150675702
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0120 -0.7856 0.0320 0.9206 3.8656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97220 0.08769 113.720 < 2e-16 ***
## category_code_LT01_1_count 0.29437 0.08956 3.287 0.00109 **
## category_code_LT01_4_count 0.90870 0.07988 11.376 < 2e-16 ***
## category_code_LT01_5_count 0.94483 0.06214 15.206 < 2e-16 ***
## category_code_LT01_9_count 0.50274 0.22477 2.237 0.02575 *
## category_code_LT01_13_count 0.05950 0.24647 0.241 0.80934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6208
## F-statistic: 163.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.620781159966836
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0123 -0.7905 0.0307 0.9172 3.8636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97181 0.08783 113.542 < 2e-16 ***
## category_code_LT01_1_count 0.29791 0.08906 3.345 0.000886 ***
## category_code_LT01_4_count 0.91104 0.08033 11.341 < 2e-16 ***
## category_code_LT01_5_count 0.94578 0.06242 15.151 < 2e-16 ***
## category_code_LT01_9_count 0.49988 0.22474 2.224 0.026586 *
## category_code_LT01_14_count -0.01649 0.33018 -0.050 0.960190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6208
## F-statistic: 163.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.620790801937557
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0121 -0.7894 0.0330 0.9181 3.8625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97186 0.08771 113.697 < 2e-16 ***
## category_code_LT01_1_count 0.29945 0.09008 3.324 0.000953 ***
## category_code_LT01_4_count 0.91110 0.07970 11.431 < 2e-16 ***
## category_code_LT01_5_count 0.94546 0.06208 15.229 < 2e-16 ***
## category_code_LT01_9_count 0.49845 0.22436 2.222 0.026763 *
## category_code_LT01_15_count -0.09332 0.76189 -0.122 0.902560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6208
## F-statistic: 163.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.621597646110251
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.7804 0.0340 0.9183 3.8627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97200 0.08760 113.837 < 2e-16 ***
## category_code_LT01_1_count 0.29895 0.08855 3.376 0.000794 ***
## category_code_LT01_4_count 0.90563 0.07960 11.377 < 2e-16 ***
## category_code_LT01_5_count 0.94382 0.06204 15.214 < 2e-16 ***
## category_code_LT01_9_count 0.48808 0.22431 2.176 0.030034 *
## category_code_LT01_16_count 1.20101 1.16427 1.032 0.302789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6216
## F-statistic: 164.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.628761250308051
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7539 0.0402 0.8997 3.4749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95094 0.09023 110.288 < 2e-16 ***
## category_code_LT01_1_count 0.24888 0.08920 2.790 0.00547 **
## category_code_LT01_4_count 0.74017 0.09319 7.943 1.35e-14 ***
## category_code_LT01_5_count 0.94036 0.06135 15.328 < 2e-16 ***
## category_code_LT01_10_count 0.15173 0.11100 1.367 0.17227
## category_code_LT01_11_count 0.39938 0.11093 3.600 0.00035 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 492 degrees of freedom
## Multiple R-squared: 0.6325, Adjusted R-squared: 0.6288
## F-statistic: 169.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.619152432177193
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9836 -0.7744 0.0383 0.9130 3.6961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93860 0.09132 108.832 < 2e-16 ***
## category_code_LT01_1_count 0.30487 0.08944 3.409 0.000706 ***
## category_code_LT01_4_count 0.91479 0.08048 11.367 < 2e-16 ***
## category_code_LT01_5_count 0.95238 0.06236 15.273 < 2e-16 ***
## category_code_LT01_10_count 0.17825 0.11228 1.588 0.113030
## category_code_LT01_12_count 0.09759 0.20708 0.471 0.637655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6192
## F-statistic: 162.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.618981777154444
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9855 -0.7744 0.0344 0.9055 3.6900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.938295 0.091352 108.791 < 2e-16 ***
## category_code_LT01_1_count 0.309876 0.089580 3.459 0.000589 ***
## category_code_LT01_4_count 0.920071 0.079860 11.521 < 2e-16 ***
## category_code_LT01_5_count 0.955477 0.062043 15.400 < 2e-16 ***
## category_code_LT01_10_count 0.180922 0.112208 1.612 0.107520
## category_code_LT01_13_count 0.009977 0.246668 0.040 0.967752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.619
## F-statistic: 162.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.619034922094089
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9840 -0.7756 0.0290 0.9021 3.6833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93550 0.09191 108.103 < 2e-16 ***
## category_code_LT01_1_count 0.31288 0.08919 3.508 0.000493 ***
## category_code_LT01_4_count 0.92277 0.08017 11.509 < 2e-16 ***
## category_code_LT01_5_count 0.95735 0.06237 15.351 < 2e-16 ***
## category_code_LT01_10_count 0.18767 0.11487 1.634 0.102940
## category_code_LT01_14_count -0.08968 0.33831 -0.265 0.791054
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.619
## F-statistic: 162.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.619060400695338
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9842 -0.7689 0.0319 0.9052 3.6838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93701 0.09141 108.712 < 2e-16 ***
## category_code_LT01_1_count 0.31556 0.09014 3.501 0.000506 ***
## category_code_LT01_4_count 0.92142 0.07971 11.560 < 2e-16 ***
## category_code_LT01_5_count 0.95549 0.06200 15.412 < 2e-16 ***
## category_code_LT01_10_count 0.18421 0.11256 1.637 0.102362
## category_code_LT01_15_count -0.24614 0.76626 -0.321 0.748180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.6191
## F-statistic: 162.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.619839126657912
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9853 -0.7605 0.0343 0.9109 3.6942
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93936 0.09124 108.935 < 2e-16 ***
## category_code_LT01_1_count 0.31158 0.08860 3.517 0.000477 ***
## category_code_LT01_4_count 0.91536 0.07969 11.486 < 2e-16 ***
## category_code_LT01_5_count 0.95368 0.06196 15.392 < 2e-16 ***
## category_code_LT01_10_count 0.17510 0.11216 1.561 0.119146
## category_code_LT01_16_count 1.23032 1.16713 1.054 0.292336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6198
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.627469920989951
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0245 -0.7700 0.0259 0.8923 3.5914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98532 0.08689 114.918 < 2e-16 ***
## category_code_LT01_1_count 0.24906 0.08964 2.779 0.005669 **
## category_code_LT01_4_count 0.75426 0.09275 8.132 3.47e-15 ***
## category_code_LT01_5_count 0.94392 0.06172 15.292 < 2e-16 ***
## category_code_LT01_11_count 0.42209 0.11462 3.683 0.000256 ***
## category_code_LT01_12_count -0.08369 0.21152 -0.396 0.692517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6275
## F-statistic: 168.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.627352103373589
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7644 0.0250 0.9048 3.6015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.984895 0.086902 114.899 < 2e-16 ***
## category_code_LT01_1_count 0.246496 0.090126 2.735 0.006463 **
## category_code_LT01_4_count 0.754688 0.092878 8.126 3.63e-15 ***
## category_code_LT01_5_count 0.941677 0.061493 15.314 < 2e-16 ***
## category_code_LT01_11_count 0.410628 0.110902 3.703 0.000238 ***
## category_code_LT01_13_count -0.007545 0.243950 -0.031 0.975340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6274
## F-statistic: 168.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.627354295246091
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7646 0.0255 0.9070 3.6019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98520 0.08702 114.749 < 2e-16 ***
## category_code_LT01_1_count 0.24558 0.08978 2.735 0.006454 **
## category_code_LT01_4_count 0.75382 0.09349 8.063 5.71e-15 ***
## category_code_LT01_5_count 0.94119 0.06183 15.223 < 2e-16 ***
## category_code_LT01_11_count 0.41046 0.11084 3.703 0.000237 ***
## category_code_LT01_14_count 0.02027 0.32666 0.062 0.950547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6274
## F-statistic: 168.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.627422832426982
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0219 -0.7639 0.0257 0.8997 3.5982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98445 0.08690 114.891 < 2e-16 ***
## category_code_LT01_1_count 0.25081 0.09063 2.767 0.005862 **
## category_code_LT01_4_count 0.75544 0.09280 8.140 3.26e-15 ***
## category_code_LT01_5_count 0.94153 0.06145 15.321 < 2e-16 ***
## category_code_LT01_11_count 0.41166 0.11089 3.712 0.000229 ***
## category_code_LT01_15_count -0.23203 0.75538 -0.307 0.758840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6274
## F-statistic: 168.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.62808182541905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0211 -0.7638 0.0335 0.8948 3.6041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98466 0.08681 115.012 < 2e-16 ***
## category_code_LT01_1_count 0.24802 0.08928 2.778 0.00568 **
## category_code_LT01_4_count 0.75150 0.09273 8.105 4.23e-15 ***
## category_code_LT01_5_count 0.94000 0.06142 15.305 < 2e-16 ***
## category_code_LT01_11_count 0.40566 0.11084 3.660 0.00028 ***
## category_code_LT01_16_count 1.13445 1.15407 0.983 0.32609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 492 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6281
## F-statistic: 168.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617207432756542
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0242 -0.7679 0.0035 0.9326 3.8545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97834 0.08806 113.310 < 2e-16 ***
## category_code_LT01_1_count 0.30158 0.09052 3.332 0.000928 ***
## category_code_LT01_4_count 0.93611 0.07974 11.740 < 2e-16 ***
## category_code_LT01_5_count 0.95359 0.06255 15.246 < 2e-16 ***
## category_code_LT01_12_count 0.11491 0.20733 0.554 0.579671
## category_code_LT01_13_count 0.02157 0.24712 0.087 0.930475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617203909949136
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0243 -0.7681 0.0034 0.9356 3.8539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97853 0.08818 113.157 < 2e-16 ***
## category_code_LT01_1_count 0.30220 0.09005 3.356 0.000852 ***
## category_code_LT01_4_count 0.93602 0.08031 11.655 < 2e-16 ***
## category_code_LT01_5_count 0.95342 0.06284 15.172 < 2e-16 ***
## category_code_LT01_12_count 0.11438 0.20773 0.551 0.582144
## category_code_LT01_14_count 0.01845 0.33174 0.056 0.955678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617221214919822
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0240 -0.7676 0.0022 0.9351 3.8523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97802 0.08807 113.295 < 2e-16 ***
## category_code_LT01_1_count 0.30529 0.09116 3.349 0.000873 ***
## category_code_LT01_4_count 0.93748 0.07966 11.768 < 2e-16 ***
## category_code_LT01_5_count 0.95380 0.06251 15.259 < 2e-16 ***
## category_code_LT01_12_count 0.11390 0.20745 0.549 0.583207
## category_code_LT01_15_count -0.12188 0.76573 -0.159 0.873601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.618192728047053
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0226 -0.7676 0.0063 0.9404 3.8531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97806 0.08795 113.458 < 2e-16 ***
## category_code_LT01_1_count 0.30404 0.08955 3.395 0.000741 ***
## category_code_LT01_4_count 0.93056 0.07958 11.694 < 2e-16 ***
## category_code_LT01_5_count 0.95172 0.06246 15.238 < 2e-16 ***
## category_code_LT01_12_count 0.11433 0.20705 0.552 0.581062
## category_code_LT01_16_count 1.32023 1.16816 1.130 0.258952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6182
## F-statistic: 161.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616975065112126
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0271 -0.7686 -0.0018 0.9181 3.8499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97908 0.08821 113.129 < 2e-16 ***
## category_code_LT01_1_count 0.30711 0.09028 3.402 0.000725 ***
## category_code_LT01_4_count 0.94185 0.07975 11.810 < 2e-16 ***
## category_code_LT01_5_count 0.95673 0.06258 15.289 < 2e-16 ***
## category_code_LT01_13_count 0.02351 0.24721 0.095 0.924262
## category_code_LT01_14_count 0.03055 0.33120 0.092 0.926538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616991404477936
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0268 -0.7679 -0.0055 0.9189 3.8480
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97836 0.08810 113.260 < 2e-16 ***
## category_code_LT01_1_count 0.31092 0.09144 3.400 0.000728 ***
## category_code_LT01_4_count 0.94390 0.07897 11.953 < 2e-16 ***
## category_code_LT01_5_count 0.95738 0.06219 15.393 < 2e-16 ***
## category_code_LT01_13_count 0.01936 0.24816 0.078 0.937836
## category_code_LT01_15_count -0.13199 0.76849 -0.172 0.863702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617969145982893
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.7680 0.0028 0.9195 3.8493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97846 0.08797 113.427 < 2e-16 ***
## category_code_LT01_1_count 0.30885 0.08969 3.443 0.000624 ***
## category_code_LT01_4_count 0.93659 0.07889 11.872 < 2e-16 ***
## category_code_LT01_5_count 0.95518 0.06214 15.370 < 2e-16 ***
## category_code_LT01_13_count 0.03202 0.24698 0.130 0.896906
## category_code_LT01_16_count 1.32719 1.16908 1.135 0.256829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616992913899292
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7683 -0.0051 0.9318 3.8475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97871 0.08822 113.114 < 2e-16 ***
## category_code_LT01_1_count 0.31120 0.09082 3.427 0.000662 ***
## category_code_LT01_4_count 0.94333 0.07962 11.848 < 2e-16 ***
## category_code_LT01_5_count 0.95694 0.06253 15.303 < 2e-16 ***
## category_code_LT01_14_count 0.02967 0.33116 0.090 0.928644
## category_code_LT01_15_count -0.13688 0.76546 -0.179 0.858155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617972225466167
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7686 0.0033 0.9335 3.8485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97903 0.08809 113.282 < 2e-16 ***
## category_code_LT01_1_count 0.30918 0.08927 3.463 0.00058 ***
## category_code_LT01_4_count 0.93564 0.07960 11.754 < 2e-16 ***
## category_code_LT01_5_count 0.95448 0.06249 15.274 < 2e-16 ***
## category_code_LT01_14_count 0.04772 0.33110 0.144 0.88546
## category_code_LT01_16_count 1.33030 1.16978 1.137 0.25600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617973913271313
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0253 -0.7677 0.0013 0.9336 3.8469
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97812 0.08798 113.409 < 2e-16 ***
## category_code_LT01_1_count 0.31290 0.09024 3.467 0.000572 ***
## category_code_LT01_4_count 0.93817 0.07877 11.911 < 2e-16 ***
## category_code_LT01_5_count 0.95547 0.06210 15.386 < 2e-16 ***
## category_code_LT01_15_count -0.11584 0.76470 -0.151 0.879660
## category_code_LT01_16_count 1.31798 1.16885 1.128 0.260045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.601713862370199
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0820 -0.8093 0.0209 0.9661 3.4229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01145 0.08982 111.455 < 2e-16 ***
## category_code_LT01_1_count 0.42074 0.08754 4.806 2.05e-06 ***
## category_code_LT01_5_count 0.98918 0.06306 15.687 < 2e-16 ***
## category_code_LT01_6_count 0.63209 0.15248 4.145 4.00e-05 ***
## category_code_LT01_7_count 0.57326 0.16048 3.572 0.000389 ***
## category_code_LT01_11_count 0.61950 0.10927 5.670 2.44e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.421 on 492 degrees of freedom
## Multiple R-squared: 0.6057, Adjusted R-squared: 0.6017
## F-statistic: 151.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.642685744844765
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9510 -0.7379 0.0446 0.8661 3.5037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94264 0.08517 116.744 < 2e-16 ***
## category_code_LT01_2_count 0.46619 0.09127 5.108 4.67e-07 ***
## category_code_LT01_3_count 0.22569 0.11239 2.008 0.0452 *
## category_code_LT01_4_count 0.58308 0.09761 5.973 4.46e-09 ***
## category_code_LT01_5_count 0.89952 0.06052 14.863 < 2e-16 ***
## category_code_LT01_6_count 0.28770 0.15013 1.916 0.0559 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 492 degrees of freedom
## Multiple R-squared: 0.6463, Adjusted R-squared: 0.6427
## F-statistic: 179.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.644951080634143
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.6968 0.0141 0.8424 3.4895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95284 0.08480 117.375 < 2e-16 ***
## category_code_LT01_2_count 0.47290 0.08961 5.277 1.97e-07 ***
## category_code_LT01_3_count 0.23331 0.11181 2.087 0.03744 *
## category_code_LT01_4_count 0.55587 0.09851 5.643 2.83e-08 ***
## category_code_LT01_5_count 0.90314 0.06010 15.027 < 2e-16 ***
## category_code_LT01_7_count 0.39279 0.15024 2.614 0.00921 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 492 degrees of freedom
## Multiple R-squared: 0.6485, Adjusted R-squared: 0.645
## F-statistic: 181.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.64020209024869
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9717 -0.7393 0.0648 0.8675 3.4765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95204 0.08540 116.538 < 2e-16 ***
## category_code_LT01_2_count 0.50370 0.08940 5.634 2.97e-08 ***
## category_code_LT01_3_count 0.24146 0.11256 2.145 0.0324 *
## category_code_LT01_4_count 0.61611 0.09649 6.385 3.96e-10 ***
## category_code_LT01_5_count 0.91574 0.06111 14.986 < 2e-16 ***
## category_code_LT01_8_count -0.13399 0.26744 -0.501 0.6166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6438, Adjusted R-squared: 0.6402
## F-statistic: 177.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.641353388823666
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9610 -0.7365 0.0413 0.8692 3.4911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94835 0.08524 116.713 < 2e-16 ***
## category_code_LT01_2_count 0.49103 0.08978 5.469 7.20e-08 ***
## category_code_LT01_3_count 0.21997 0.11333 1.941 0.0528 .
## category_code_LT01_4_count 0.61069 0.09639 6.336 5.33e-10 ***
## category_code_LT01_5_count 0.90574 0.06046 14.981 < 2e-16 ***
## category_code_LT01_9_count 0.30022 0.22186 1.353 0.1766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 492 degrees of freedom
## Multiple R-squared: 0.645, Adjusted R-squared: 0.6414
## F-statistic: 178.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.640293032529895
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9530 -0.7261 0.0416 0.8648 3.4222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93655 0.08842 112.374 < 2e-16 ***
## category_code_LT01_2_count 0.50049 0.08959 5.587 3.84e-08 ***
## category_code_LT01_3_count 0.22848 0.11411 2.002 0.0458 *
## category_code_LT01_4_count 0.61478 0.09647 6.373 4.28e-10 ***
## category_code_LT01_5_count 0.91113 0.06042 15.081 < 2e-16 ***
## category_code_LT01_10_count 0.06811 0.11115 0.613 0.5403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6403
## F-statistic: 177.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.642096188255638
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.7425 0.0353 0.8456 3.4780
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95794 0.08524 116.824 < 2e-16 ***
## category_code_LT01_2_count 0.44705 0.09535 4.688 3.57e-06 ***
## category_code_LT01_3_count 0.20304 0.11436 1.776 0.0764 .
## category_code_LT01_4_count 0.56715 0.10037 5.651 2.71e-08 ***
## category_code_LT01_5_count 0.90897 0.06028 15.080 < 2e-16 ***
## category_code_LT01_11_count 0.19978 0.11821 1.690 0.0917 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 492 degrees of freedom
## Multiple R-squared: 0.6457, Adjusted R-squared: 0.6421
## F-statistic: 179.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.64001868128848
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9672 -0.7300 0.0665 0.8724 3.4827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.950728 0.085379 116.548 < 2e-16 ***
## category_code_LT01_2_count 0.504322 0.090222 5.590 3.77e-08 ***
## category_code_LT01_3_count 0.240222 0.112710 2.131 0.0336 *
## category_code_LT01_4_count 0.615489 0.096707 6.364 4.49e-10 ***
## category_code_LT01_5_count 0.911255 0.060676 15.018 < 2e-16 ***
## category_code_LT01_12_count -0.002912 0.201848 -0.014 0.9885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.64
## F-statistic: 177.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.640072235910808
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9669 -0.7366 0.0596 0.8728 3.4832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95070 0.08537 116.557 < 2e-16 ***
## category_code_LT01_2_count 0.50305 0.08951 5.620 3.20e-08 ***
## category_code_LT01_3_count 0.23986 0.11255 2.131 0.0336 *
## category_code_LT01_4_count 0.61302 0.09689 6.327 5.63e-10 ***
## category_code_LT01_5_count 0.91079 0.06045 15.067 < 2e-16 ***
## category_code_LT01_13_count 0.06436 0.23754 0.271 0.7865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6401
## F-statistic: 177.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.640059347287083
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9670 -0.7252 0.0714 0.8729 3.4835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95162 0.08546 116.452 < 2e-16 ***
## category_code_LT01_2_count 0.50279 0.08960 5.611 3.36e-08 ***
## category_code_LT01_3_count 0.24156 0.11271 2.143 0.0326 *
## category_code_LT01_4_count 0.61195 0.09759 6.270 7.89e-10 ***
## category_code_LT01_5_count 0.90968 0.06077 14.970 < 2e-16 ***
## category_code_LT01_14_count 0.07565 0.32029 0.236 0.8134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6401
## F-statistic: 177.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.640054769550945
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9667 -0.7371 0.0723 0.8730 3.4834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95053 0.08538 116.546 < 2e-16 ***
## category_code_LT01_2_count 0.50489 0.08948 5.642 2.83e-08 ***
## category_code_LT01_3_count 0.24315 0.11336 2.145 0.0324 *
## category_code_LT01_4_count 0.61626 0.09657 6.381 4.06e-10 ***
## category_code_LT01_5_count 0.91082 0.06046 15.066 < 2e-16 ***
## category_code_LT01_15_count -0.16407 0.73718 -0.223 0.8240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6401
## F-statistic: 177.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.640035664938674
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9673 -0.7329 0.0687 0.8724 3.4827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95093 0.08539 116.539 < 2e-16 ***
## category_code_LT01_2_count 0.50287 0.08981 5.599 3.59e-08 ***
## category_code_LT01_3_count 0.23838 0.11313 2.107 0.0356 *
## category_code_LT01_4_count 0.61641 0.09673 6.373 4.27e-10 ***
## category_code_LT01_5_count 0.91103 0.06044 15.072 < 2e-16 ***
## category_code_LT01_16_count 0.17572 1.14820 0.153 0.8784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.64
## F-statistic: 177.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.628064261355259
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9877 -0.7488 0.0036 0.8686 3.8406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96038 0.08684 114.699 < 2e-16 ***
## category_code_LT01_2_count 0.65778 0.08306 7.919 1.6e-14 ***
## category_code_LT01_3_count 0.40586 0.10866 3.735 0.000210 ***
## category_code_LT01_5_count 0.92686 0.06143 15.087 < 2e-16 ***
## category_code_LT01_6_count 0.42841 0.15091 2.839 0.004714 **
## category_code_LT01_7_count 0.57839 0.14965 3.865 0.000126 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 492 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6281
## F-statistic: 168.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.616979482096433
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0055 -0.7612 0.0182 0.8756 3.8394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96151 0.08816 112.992 < 2e-16 ***
## category_code_LT01_2_count 0.74717 0.08091 9.234 < 2e-16 ***
## category_code_LT01_3_count 0.45200 0.10967 4.121 4.42e-05 ***
## category_code_LT01_5_count 0.95093 0.06282 15.138 < 2e-16 ***
## category_code_LT01_6_count 0.44596 0.15323 2.910 0.00377 **
## category_code_LT01_8_count -0.14254 0.27615 -0.516 0.60598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.618343945751164
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9943 -0.7654 0.0317 0.8783 3.8433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95768 0.08798 113.176 < 2e-16 ***
## category_code_LT01_2_count 0.73253 0.08147 8.991 < 2e-16 ***
## category_code_LT01_3_count 0.42742 0.11065 3.863 0.000127 ***
## category_code_LT01_5_count 0.94021 0.06217 15.124 < 2e-16 ***
## category_code_LT01_6_count 0.43424 0.15294 2.839 0.004708 **
## category_code_LT01_9_count 0.32582 0.22889 1.423 0.155228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6183
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.616823085229207
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7660 -0.0066 0.8957 3.8468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95419 0.09121 109.139 < 2e-16 ***
## category_code_LT01_2_count 0.74720 0.08096 9.229 < 2e-16 ***
## category_code_LT01_3_count 0.44595 0.11116 4.012 6.96e-05 ***
## category_code_LT01_5_count 0.94640 0.06216 15.226 < 2e-16 ***
## category_code_LT01_6_count 0.43710 0.15465 2.826 0.0049 **
## category_code_LT01_10_count 0.02965 0.11585 0.256 0.7981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.623519121313562
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7745 0.0494 0.8412 3.8293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97163 0.08745 114.023 < 2e-16 ***
## category_code_LT01_2_count 0.61565 0.09174 6.711 5.34e-11 ***
## category_code_LT01_3_count 0.35745 0.11313 3.160 0.00168 **
## category_code_LT01_5_count 0.93773 0.06167 15.206 < 2e-16 ***
## category_code_LT01_6_count 0.37800 0.15333 2.465 0.01403 *
## category_code_LT01_11_count 0.34868 0.11743 2.969 0.00313 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 492 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6235
## F-statistic: 165.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.616777780454032
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0006 -0.7616 0.0226 0.8868 3.8408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96018 0.08815 112.995 < 2e-16 ***
## category_code_LT01_2_count 0.74678 0.08193 9.115 < 2e-16 ***
## category_code_LT01_3_count 0.44998 0.10991 4.094 4.95e-05 ***
## category_code_LT01_5_count 0.94571 0.06238 15.160 < 2e-16 ***
## category_code_LT01_6_count 0.44126 0.15398 2.866 0.00434 **
## category_code_LT01_12_count 0.01787 0.20897 0.086 0.93187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.617318679058539
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9991 -0.7554 0.0144 0.8969 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95979 0.08809 113.069 < 2e-16 ***
## category_code_LT01_2_count 0.74021 0.08138 9.096 < 2e-16 ***
## category_code_LT01_3_count 0.44653 0.10970 4.071 5.46e-05 ***
## category_code_LT01_5_count 0.94420 0.06215 15.192 < 2e-16 ***
## category_code_LT01_6_count 0.44339 0.15303 2.897 0.00393 **
## category_code_LT01_13_count 0.20449 0.24393 0.838 0.40227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.618210941554467
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9969 -0.7638 -0.0193 0.8995 3.8368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96417 0.08803 113.190 < 2e-16 ***
## category_code_LT01_2_count 0.72620 0.08233 8.821 < 2e-16 ***
## category_code_LT01_3_count 0.44922 0.10947 4.104 4.76e-05 ***
## category_code_LT01_5_count 0.93471 0.06260 14.931 < 2e-16 ***
## category_code_LT01_6_count 0.45743 0.15323 2.985 0.00297 **
## category_code_LT01_14_count 0.44524 0.32698 1.362 0.17392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6182
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.61677713905832
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0007 -0.7635 0.0223 0.8872 3.8408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96011 0.08815 112.988 < 2e-16 ***
## category_code_LT01_2_count 0.74823 0.08104 9.232 < 2e-16 ***
## category_code_LT01_3_count 0.45181 0.11070 4.082 5.22e-05 ***
## category_code_LT01_5_count 0.94604 0.06217 15.217 < 2e-16 ***
## category_code_LT01_6_count 0.44312 0.15325 2.891 0.004 **
## category_code_LT01_15_count -0.06129 0.76061 -0.081 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.616773615206092
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7631 0.0221 0.8880 3.8407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96023 0.08815 112.986 < 2e-16 ***
## category_code_LT01_2_count 0.74751 0.08135 9.189 < 2e-16 ***
## category_code_LT01_3_count 0.45013 0.11019 4.085 5.14e-05 ***
## category_code_LT01_5_count 0.94611 0.06216 15.220 < 2e-16 ***
## category_code_LT01_6_count 0.44340 0.15407 2.878 0.00418 **
## category_code_LT01_16_count 0.05276 1.18918 0.044 0.96463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.622191165594111
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0214 -0.7363 -0.0012 0.8301 3.8252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97574 0.08743 114.104 < 2e-16 ***
## category_code_LT01_2_count 0.73782 0.07868 9.377 < 2e-16 ***
## category_code_LT01_3_count 0.44788 0.10860 4.124 4.37e-05 ***
## category_code_LT01_5_count 0.95333 0.06214 15.341 < 2e-16 ***
## category_code_LT01_7_count 0.59159 0.15088 3.921 0.000101 ***
## category_code_LT01_8_count -0.14659 0.27420 -0.535 0.593155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6222
## F-statistic: 164.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.623030902740002
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0110 -0.7581 -0.0131 0.8331 3.8290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97198 0.08731 114.217 < 2e-16 ***
## category_code_LT01_2_count 0.72710 0.07917 9.184 < 2e-16 ***
## category_code_LT01_3_count 0.42793 0.10957 3.906 0.000107 ***
## category_code_LT01_5_count 0.94369 0.06150 15.343 < 2e-16 ***
## category_code_LT01_7_count 0.57176 0.15131 3.779 0.000177 ***
## category_code_LT01_9_count 0.26849 0.22835 1.176 0.240256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.623
## F-statistic: 165.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.62208647579957
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0073 -0.7457 -0.0272 0.8379 3.8359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96507 0.09058 110.017 < 2e-16 ***
## category_code_LT01_2_count 0.73643 0.07884 9.341 < 2e-16 ***
## category_code_LT01_3_count 0.43895 0.11025 3.981 7.89e-05 ***
## category_code_LT01_5_count 0.94843 0.06145 15.433 < 2e-16 ***
## category_code_LT01_7_count 0.58460 0.15119 3.867 0.000125 ***
## category_code_LT01_10_count 0.04415 0.11421 0.387 0.699234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6221
## F-statistic: 164.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.62629164858676
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0200 -0.7696 0.0221 0.8169 3.8189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98201 0.08697 114.778 < 2e-16 ***
## category_code_LT01_2_count 0.63555 0.08933 7.114 3.99e-12 ***
## category_code_LT01_3_count 0.37261 0.11231 3.318 0.000975 ***
## category_code_LT01_5_count 0.94221 0.06116 15.405 < 2e-16 ***
## category_code_LT01_7_count 0.48730 0.15588 3.126 0.001876 **
## category_code_LT01_11_count 0.28708 0.12038 2.385 0.017465 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6263
## F-statistic: 167.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.622065137494593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0148 -0.7562 -0.0111 0.8302 3.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97407 0.08740 114.120 < 2e-16 ***
## category_code_LT01_2_count 0.73297 0.08019 9.141 < 2e-16 ***
## category_code_LT01_3_count 0.44335 0.10892 4.070 5.47e-05 ***
## category_code_LT01_5_count 0.94625 0.06175 15.324 < 2e-16 ***
## category_code_LT01_7_count 0.58819 0.15082 3.900 0.00011 ***
## category_code_LT01_12_count 0.07199 0.20639 0.349 0.72739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6221
## F-statistic: 164.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.622049250661738
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0160 -0.7607 -0.0148 0.8385 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97413 0.08740 114.120 < 2e-16 ***
## category_code_LT01_2_count 0.73651 0.07890 9.334 < 2e-16 ***
## category_code_LT01_3_count 0.44533 0.10863 4.099 4.85e-05 ***
## category_code_LT01_5_count 0.94784 0.06148 15.418 < 2e-16 ***
## category_code_LT01_7_count 0.58242 0.15212 3.829 0.000146 ***
## category_code_LT01_13_count 0.07770 0.24453 0.318 0.750802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.622
## F-statistic: 164.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.622443639088728
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7468 -0.0016 0.8221 3.8241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97684 0.08742 114.131 < 2e-16 ***
## category_code_LT01_2_count 0.72939 0.07947 9.178 < 2e-16 ***
## category_code_LT01_3_count 0.44729 0.10854 4.121 4.42e-05 ***
## category_code_LT01_5_count 0.94261 0.06186 15.238 < 2e-16 ***
## category_code_LT01_7_count 0.57723 0.15145 3.811 0.000156 ***
## category_code_LT01_14_count 0.25556 0.32588 0.784 0.433285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6224
## F-statistic: 164.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.62198674362359
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0167 -0.7559 -0.0170 0.8282 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97437 0.08741 114.110 < 2e-16 ***
## category_code_LT01_2_count 0.73748 0.07894 9.342 < 2e-16 ***
## category_code_LT01_3_count 0.44412 0.10975 4.047 6.03e-05 ***
## category_code_LT01_5_count 0.94851 0.06147 15.430 < 2e-16 ***
## category_code_LT01_7_count 0.58934 0.15088 3.906 0.000107 ***
## category_code_LT01_15_count 0.10572 0.75513 0.140 0.888720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6258, Adjusted R-squared: 0.622
## F-statistic: 164.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.621989787540263
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0163 -0.7584 -0.0177 0.8368 3.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97402 0.08742 114.091 < 2e-16 ***
## category_code_LT01_2_count 0.73923 0.07890 9.369 < 2e-16 ***
## category_code_LT01_3_count 0.44777 0.10899 4.108 4.67e-05 ***
## category_code_LT01_5_count 0.94843 0.06146 15.431 < 2e-16 ***
## category_code_LT01_7_count 0.58803 0.15090 3.897 0.000111 ***
## category_code_LT01_16_count -0.18028 1.17446 -0.153 0.878070
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6258, Adjusted R-squared: 0.622
## F-statistic: 164.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.61224404354735
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0273 -0.7623 0.0255 0.8764 3.8282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97275 0.08859 112.572 < 2e-16 ***
## category_code_LT01_2_count 0.81434 0.07678 10.606 < 2e-16 ***
## category_code_LT01_3_count 0.46868 0.11065 4.235 2.72e-05 ***
## category_code_LT01_5_count 0.96605 0.06290 15.358 < 2e-16 ***
## category_code_LT01_8_count -0.12271 0.27776 -0.442 0.659
## category_code_LT01_9_count 0.35424 0.23066 1.536 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6122
## F-statistic: 157.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.610731135982143
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0187 -0.7557 0.0206 0.8354 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95973 0.09194 108.325 < 2e-16 ***
## category_code_LT01_2_count 0.82831 0.07629 10.858 < 2e-16 ***
## category_code_LT01_3_count 0.48126 0.11141 4.320 1.89e-05 ***
## category_code_LT01_5_count 0.97252 0.06288 15.465 < 2e-16 ***
## category_code_LT01_8_count -0.11254 0.27821 -0.405 0.686
## category_code_LT01_10_count 0.07647 0.11564 0.661 0.509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6107
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.618953664839687
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0354 -0.7683 -0.0070 0.8221 4.0601
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98588 0.08785 113.664 < 2e-16 ***
## category_code_LT01_2_count 0.67143 0.08947 7.504 2.92e-13 ***
## category_code_LT01_3_count 0.38349 0.11344 3.381 0.000781 ***
## category_code_LT01_5_count 0.95881 0.06235 15.377 < 2e-16 ***
## category_code_LT01_8_count -0.09115 0.27525 -0.331 0.740673
## category_code_LT01_11_count 0.38903 0.11696 3.326 0.000946 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.619
## F-statistic: 162.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.610512004973453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0328 -0.7650 0.0030 0.8604 3.8255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97545 0.08877 112.376 < 2e-16 ***
## category_code_LT01_2_count 0.82641 0.07766 10.641 < 2e-16 ***
## category_code_LT01_3_count 0.49110 0.10995 4.467 9.87e-06 ***
## category_code_LT01_5_count 0.97018 0.06317 15.358 < 2e-16 ***
## category_code_LT01_8_count -0.11301 0.27843 -0.406 0.685
## category_code_LT01_12_count 0.08392 0.20966 0.400 0.689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6105
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.610883712841485
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0325 -0.7748 0.0083 0.8839 3.8258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97515 0.08873 112.425 < 2e-16 ***
## category_code_LT01_2_count 0.82560 0.07651 10.791 < 2e-16 ***
## category_code_LT01_3_count 0.49062 0.10967 4.474 9.56e-06 ***
## category_code_LT01_5_count 0.97023 0.06294 15.416 < 2e-16 ***
## category_code_LT01_8_count -0.09639 0.27854 -0.346 0.729
## category_code_LT01_13_count 0.19560 0.24636 0.794 0.428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6109
## F-statistic: 157.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.61143004418557
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0323 -0.7690 0.0203 0.8475 3.8215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97949 0.08873 112.475 < 2e-16 ***
## category_code_LT01_2_count 0.81677 0.07719 10.582 < 2e-16 ***
## category_code_LT01_3_count 0.49470 0.10948 4.519 7.8e-06 ***
## category_code_LT01_5_count 0.96362 0.06330 15.223 < 2e-16 ***
## category_code_LT01_8_count -0.11464 0.27795 -0.412 0.680
## category_code_LT01_14_count 0.37853 0.32909 1.150 0.251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6114
## F-statistic: 157.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.610386165138479
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.7652 0.0082 0.8628 3.8253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97566 0.08879 112.357 < 2e-16 ***
## category_code_LT01_2_count 0.83259 0.07623 10.922 < 2e-16 ***
## category_code_LT01_3_count 0.49403 0.11074 4.461 1.01e-05 ***
## category_code_LT01_5_count 0.97256 0.06293 15.455 < 2e-16 ***
## category_code_LT01_8_count -0.10890 0.27829 -0.391 0.696
## category_code_LT01_15_count 0.02724 0.76638 0.036 0.972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6104
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.610436032533492
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0341 -0.7647 0.0042 0.8583 3.8258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97518 0.08879 112.340 < 2e-16 ***
## category_code_LT01_2_count 0.83407 0.07618 10.948 < 2e-16 ***
## category_code_LT01_3_count 0.49685 0.10998 4.518 7.83e-06 ***
## category_code_LT01_5_count 0.97248 0.06291 15.459 < 2e-16 ***
## category_code_LT01_8_count -0.10542 0.27858 -0.378 0.705
## category_code_LT01_16_count -0.30239 1.19305 -0.253 0.800
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6104
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.612293718618744
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.7489 0.0347 0.8733 3.8416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95937 0.09173 108.573 < 2e-16 ***
## category_code_LT01_2_count 0.81173 0.07698 10.544 < 2e-16 ***
## category_code_LT01_3_count 0.45798 0.11217 4.083 5.19e-05 ***
## category_code_LT01_5_count 0.96201 0.06218 15.471 < 2e-16 ***
## category_code_LT01_9_count 0.33942 0.23163 1.465 0.143
## category_code_LT01_10_count 0.05892 0.11594 0.508 0.612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6123
## F-statistic: 158 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.620418742187549
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0251 -0.7716 0.0186 0.8138 4.1200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98207 0.08767 113.862 < 2e-16 ***
## category_code_LT01_2_count 0.65683 0.08987 7.309 1.1e-12 ***
## category_code_LT01_3_count 0.36009 0.11422 3.153 0.00172 **
## category_code_LT01_5_count 0.94950 0.06164 15.403 < 2e-16 ***
## category_code_LT01_9_count 0.32352 0.22825 1.417 0.15699
## category_code_LT01_11_count 0.38373 0.11679 3.286 0.00109 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6204
## F-statistic: 163.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.612208759821449
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0213 -0.7609 0.0300 0.8796 3.8296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97133 0.08855 112.602 < 2e-16 ***
## category_code_LT01_2_count 0.80841 0.07842 10.309 < 2e-16 ***
## category_code_LT01_3_count 0.46402 0.11098 4.181 3.43e-05 ***
## category_code_LT01_5_count 0.95948 0.06250 15.353 < 2e-16 ***
## category_code_LT01_9_count 0.35102 0.23055 1.523 0.129
## category_code_LT01_12_count 0.08108 0.20906 0.388 0.698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 492 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6122
## F-statistic: 157.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.612747579183146
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0210 -0.7606 0.0230 0.9006 3.8299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97102 0.08849 112.677 < 2e-16 ***
## category_code_LT01_2_count 0.80562 0.07736 10.414 < 2e-16 ***
## category_code_LT01_3_count 0.46208 0.11070 4.174 3.54e-05 ***
## category_code_LT01_5_count 0.95950 0.06220 15.426 < 2e-16 ***
## category_code_LT01_9_count 0.36414 0.23084 1.577 0.115
## category_code_LT01_13_count 0.22469 0.24587 0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6166, Adjusted R-squared: 0.6127
## F-statistic: 158.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.612932143342268
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0213 -0.7647 0.0222 0.8692 3.8258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97512 0.08854 112.667 < 2e-16 ***
## category_code_LT01_2_count 0.80109 0.07781 10.295 < 2e-16 ***
## category_code_LT01_3_count 0.46879 0.11053 4.241 2.66e-05 ***
## category_code_LT01_5_count 0.95404 0.06259 15.242 < 2e-16 ***
## category_code_LT01_9_count 0.33312 0.23097 1.442 0.150
## category_code_LT01_14_count 0.34067 0.32931 1.034 0.301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6129
## F-statistic: 158.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.612096910908694
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0233 -0.7712 0.0229 0.8858 3.8293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97161 0.08857 112.588 < 2e-16 ***
## category_code_LT01_2_count 0.81402 0.07704 10.567 < 2e-16 ***
## category_code_LT01_3_count 0.46593 0.11185 4.166 3.67e-05 ***
## category_code_LT01_5_count 0.96198 0.06221 15.464 < 2e-16 ***
## category_code_LT01_9_count 0.35176 0.23076 1.524 0.128
## category_code_LT01_15_count 0.07055 0.76526 0.092 0.927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 492 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6121
## F-statistic: 157.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.61215909829714
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0227 -0.7641 0.0219 0.8765 3.8299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97105 0.08857 112.574 < 2e-16 ***
## category_code_LT01_2_count 0.81601 0.07694 10.605 < 2e-16 ***
## category_code_LT01_3_count 0.47004 0.11098 4.235 2.72e-05 ***
## category_code_LT01_5_count 0.96196 0.06219 15.467 < 2e-16 ***
## category_code_LT01_9_count 0.35195 0.23059 1.526 0.128
## category_code_LT01_16_count -0.35154 1.18919 -0.296 0.768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 492 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6122
## F-statistic: 157.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.61919986187474
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0166 -0.7590 0.0025 0.8366 4.1019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96939 0.09096 109.597 < 2e-16 ***
## category_code_LT01_2_count 0.66684 0.08969 7.435 4.67e-13 ***
## category_code_LT01_3_count 0.36914 0.11507 3.208 0.001424 **
## category_code_LT01_5_count 0.95552 0.06159 15.514 < 2e-16 ***
## category_code_LT01_10_count 0.07480 0.11435 0.654 0.513366
## category_code_LT01_11_count 0.38963 0.11690 3.333 0.000924 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6192
## F-statistic: 162.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.610708076048813
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0136 -0.7495 0.0082 0.8491 3.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95885 0.09192 108.347 < 2e-16 ***
## category_code_LT01_2_count 0.82265 0.07790 10.561 < 2e-16 ***
## category_code_LT01_3_count 0.47704 0.11168 4.271 2.33e-05 ***
## category_code_LT01_5_count 0.96637 0.06245 15.474 < 2e-16 ***
## category_code_LT01_10_count 0.07430 0.11567 0.642 0.521
## category_code_LT01_12_count 0.07685 0.20955 0.367 0.714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6107
## F-statistic: 156.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.611105064064972
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0139 -0.7506 0.0165 0.8517 3.8420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95894 0.09187 108.403 < 2e-16 ***
## category_code_LT01_2_count 0.82134 0.07677 10.699 < 2e-16 ***
## category_code_LT01_3_count 0.47666 0.11140 4.279 2.26e-05 ***
## category_code_LT01_5_count 0.96678 0.06216 15.554 < 2e-16 ***
## category_code_LT01_10_count 0.07310 0.11560 0.632 0.527
## category_code_LT01_13_count 0.19631 0.24599 0.798 0.425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.615, Adjusted R-squared: 0.6111
## F-statistic: 157.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.611428732725759
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0184 -0.7574 0.0164 0.8574 3.8331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96787 0.09226 108.043 < 2e-16 ***
## category_code_LT01_2_count 0.81539 0.07728 10.552 < 2e-16 ***
## category_code_LT01_3_count 0.48481 0.11137 4.353 1.63e-05 ***
## category_code_LT01_5_count 0.96038 0.06261 15.339 < 2e-16 ***
## category_code_LT01_10_count 0.04862 0.11847 0.410 0.682
## category_code_LT01_14_count 0.34532 0.33745 1.023 0.307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6114
## F-statistic: 157.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.610601700023214
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7522 0.0109 0.8401 3.8422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958785 0.091955 108.300 < 2e-16 ***
## category_code_LT01_2_count 0.828459 0.076479 10.833 < 2e-16 ***
## category_code_LT01_3_count 0.480169 0.112343 4.274 2.3e-05 ***
## category_code_LT01_5_count 0.968624 0.062167 15.581 < 2e-16 ***
## category_code_LT01_10_count 0.075565 0.115839 0.652 0.514
## category_code_LT01_15_count -0.005179 0.767466 -0.007 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6106
## F-statistic: 156.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.61066631120853
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0145 -0.7477 0.0102 0.8392 3.8428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95818 0.09195 108.303 < 2e-16 ***
## category_code_LT01_2_count 0.82982 0.07645 10.854 < 2e-16 ***
## category_code_LT01_3_count 0.48254 0.11172 4.319 1.89e-05 ***
## category_code_LT01_5_count 0.96873 0.06215 15.587 < 2e-16 ***
## category_code_LT01_10_count 0.07622 0.11565 0.659 0.510
## category_code_LT01_16_count -0.34059 1.19160 -0.286 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6107
## F-statistic: 156.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.619005387850976
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0344 -0.7696 -0.0129 0.8170 4.0583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98555 0.08782 113.709 < 2e-16 ***
## category_code_LT01_2_count 0.67296 0.08957 7.513 2.74e-13 ***
## category_code_LT01_3_count 0.38251 0.11336 3.374 0.000799 ***
## category_code_LT01_5_count 0.95787 0.06184 15.491 < 2e-16 ***
## category_code_LT01_11_count 0.40188 0.12043 3.337 0.000911 ***
## category_code_LT01_12_count -0.08965 0.21341 -0.420 0.674611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.619
## F-statistic: 162.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.619148908234704
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7742 0.0047 0.8332 4.0722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98459 0.08779 113.729 < 2e-16 ***
## category_code_LT01_2_count 0.66772 0.08963 7.450 4.23e-13 ***
## category_code_LT01_3_count 0.38066 0.11337 3.358 0.000847 ***
## category_code_LT01_5_count 0.95440 0.06163 15.487 < 2e-16 ***
## category_code_LT01_11_count 0.38506 0.11717 3.286 0.001088 **
## category_code_LT01_13_count 0.14673 0.24389 0.602 0.547708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6191
## F-statistic: 162.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.619580672560501
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0302 -0.7743 0.0119 0.8225 4.0797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98795 0.08779 113.765 < 2e-16 ***
## category_code_LT01_2_count 0.66067 0.09006 7.336 9.16e-13 ***
## category_code_LT01_3_count 0.38411 0.11329 3.390 0.000754 ***
## category_code_LT01_5_count 0.94833 0.06203 15.289 < 2e-16 ***
## category_code_LT01_11_count 0.38314 0.11705 3.273 0.001137 **
## category_code_LT01_14_count 0.31295 0.32614 0.960 0.337748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6196
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.618873007359811
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0321 -0.7738 -0.0028 0.8257 4.0612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98494 0.08782 113.692 < 2e-16 ***
## category_code_LT01_2_count 0.67146 0.08957 7.496 3.08e-13 ***
## category_code_LT01_3_count 0.38326 0.11431 3.353 0.000861 ***
## category_code_LT01_5_count 0.95553 0.06163 15.504 < 2e-16 ***
## category_code_LT01_11_count 0.39005 0.11701 3.334 0.000922 ***
## category_code_LT01_15_count -0.05631 0.75834 -0.074 0.940833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6189
## F-statistic: 162.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.618881027132609
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0321 -0.7735 -0.0099 0.8242 4.0599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98476 0.08784 113.671 < 2e-16 ***
## category_code_LT01_2_count 0.67206 0.08976 7.487 3.28e-13 ***
## category_code_LT01_3_count 0.38350 0.11386 3.368 0.000816 ***
## category_code_LT01_5_count 0.95569 0.06162 15.510 < 2e-16 ***
## category_code_LT01_11_count 0.38912 0.11707 3.324 0.000954 ***
## category_code_LT01_16_count -0.14863 1.17988 -0.126 0.899809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 492 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6189
## F-statistic: 162.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.610893108963177
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0275 -0.7635 0.0056 0.8862 3.8270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97397 0.08868 112.468 < 2e-16 ***
## category_code_LT01_2_count 0.81978 0.07810 10.497 < 2e-16 ***
## category_code_LT01_3_count 0.48621 0.10995 4.422 1.2e-05 ***
## category_code_LT01_5_count 0.96464 0.06247 15.441 < 2e-16 ***
## category_code_LT01_12_count 0.07601 0.20949 0.363 0.717
## category_code_LT01_13_count 0.19789 0.24607 0.804 0.422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6109
## F-statistic: 157.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.611364165162538
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7676 0.0110 0.8425 3.8229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97807 0.08869 112.503 < 2e-16 ***
## category_code_LT01_2_count 0.81256 0.07861 10.337 < 2e-16 ***
## category_code_LT01_3_count 0.49071 0.10979 4.470 9.74e-06 ***
## category_code_LT01_5_count 0.95808 0.06284 15.246 < 2e-16 ***
## category_code_LT01_12_count 0.06182 0.20997 0.294 0.769
## category_code_LT01_14_count 0.36821 0.33014 1.115 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6114
## F-statistic: 157.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.610382995221494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0291 -0.7639 -0.0053 0.8711 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97435 0.08874 112.396 < 2e-16 ***
## category_code_LT01_2_count 0.82645 0.07791 10.608 < 2e-16 ***
## category_code_LT01_3_count 0.48920 0.11108 4.404 1.31e-05 ***
## category_code_LT01_5_count 0.96641 0.06249 15.466 < 2e-16 ***
## category_code_LT01_12_count 0.08095 0.20962 0.386 0.700
## category_code_LT01_15_count 0.03229 0.76662 0.042 0.966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6104
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610435884038503
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.7635 -0.0075 0.8678 3.8271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97389 0.08875 112.383 <2e-16 ***
## category_code_LT01_2_count 0.82813 0.07786 10.636 <2e-16 ***
## category_code_LT01_3_count 0.49231 0.11031 4.463 1e-05 ***
## category_code_LT01_5_count 0.96650 0.06248 15.470 <2e-16 ***
## category_code_LT01_12_count 0.07927 0.20961 0.378 0.705
## category_code_LT01_16_count -0.31216 1.19210 -0.262 0.794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6104
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611805626953254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0268 -0.7675 0.0133 0.8782 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97792 0.08864 112.568 < 2e-16 ***
## category_code_LT01_2_count 0.80978 0.07766 10.427 < 2e-16 ***
## category_code_LT01_3_count 0.48946 0.10948 4.471 9.68e-06 ***
## category_code_LT01_5_count 0.95791 0.06260 15.302 < 2e-16 ***
## category_code_LT01_13_count 0.19752 0.24569 0.804 0.422
## category_code_LT01_14_count 0.37333 0.32890 1.135 0.257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 492 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.6118
## F-statistic: 157.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.61079448712629
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0294 -0.7698 0.0090 0.8928 3.8267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97423 0.08870 112.455 < 2e-16 ***
## category_code_LT01_2_count 0.82496 0.07677 10.746 < 2e-16 ***
## category_code_LT01_3_count 0.48803 0.11079 4.405 1.3e-05 ***
## category_code_LT01_5_count 0.96695 0.06219 15.548 < 2e-16 ***
## category_code_LT01_13_count 0.20168 0.24649 0.818 0.414
## category_code_LT01_15_count 0.06389 0.76746 0.083 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.6108
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610832502669635
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0289 -0.7633 0.0028 0.8870 3.8272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97379 0.08870 112.440 < 2e-16 ***
## category_code_LT01_2_count 0.82675 0.07671 10.778 < 2e-16 ***
## category_code_LT01_3_count 0.49154 0.11001 4.468 9.79e-06 ***
## category_code_LT01_5_count 0.96696 0.06218 15.551 < 2e-16 ***
## category_code_LT01_13_count 0.19771 0.24625 0.803 0.422
## category_code_LT01_16_count -0.27961 1.19236 -0.235 0.815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.6108
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.611295749040611
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0284 -0.7679 0.0159 0.8544 3.8226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.978318 0.088698 112.497 < 2e-16 ***
## category_code_LT01_2_count 0.816888 0.077381 10.557 < 2e-16 ***
## category_code_LT01_3_count 0.493185 0.110568 4.460 1.01e-05 ***
## category_code_LT01_5_count 0.959724 0.062616 15.327 < 2e-16 ***
## category_code_LT01_14_count 0.375992 0.329162 1.142 0.254
## category_code_LT01_15_count 0.006521 0.765605 0.009 0.993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6113
## F-statistic: 157.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.611328838597944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0281 -0.7675 0.0152 0.8497 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97793 0.08871 112.476 < 2e-16 ***
## category_code_LT01_2_count 0.81813 0.07742 10.568 < 2e-16 ***
## category_code_LT01_3_count 0.49518 0.10981 4.509 8.14e-06 ***
## category_code_LT01_5_count 0.95988 0.06260 15.333 < 2e-16 ***
## category_code_LT01_14_count 0.37205 0.32966 1.129 0.260
## category_code_LT01_16_count -0.24425 1.19242 -0.205 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6113
## F-statistic: 157.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610322893703739
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0305 -0.7637 0.0076 0.8661 3.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97410 0.08876 112.366 < 2e-16 ***
## category_code_LT01_2_count 0.83410 0.07642 10.915 < 2e-16 ***
## category_code_LT01_3_count 0.49545 0.11115 4.458 1.03e-05 ***
## category_code_LT01_5_count 0.96886 0.06219 15.579 < 2e-16 ***
## category_code_LT01_15_count 0.01389 0.76738 0.018 0.986
## category_code_LT01_16_count -0.32290 1.19338 -0.271 0.787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6103
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.644995652619049
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9594 -0.7162 0.0178 0.8651 3.4954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95127 0.08482 117.316 < 2e-16 ***
## category_code_LT01_2_count 0.47086 0.08980 5.244 2.34e-07 ***
## category_code_LT01_4_count 0.58453 0.09472 6.171 1.42e-09 ***
## category_code_LT01_5_count 0.89922 0.06026 14.921 < 2e-16 ***
## category_code_LT01_6_count 0.31380 0.14932 2.102 0.03610 *
## category_code_LT01_7_count 0.40472 0.15021 2.694 0.00729 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 492 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.645
## F-statistic: 181.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.639967528513823
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9685 -0.7404 0.0910 0.8935 3.4808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95085 0.08546 116.441 < 2e-16 ***
## category_code_LT01_2_count 0.50451 0.08952 5.635 2.94e-08 ***
## category_code_LT01_4_count 0.64951 0.09233 7.035 6.73e-12 ***
## category_code_LT01_5_count 0.91285 0.06126 14.901 < 2e-16 ***
## category_code_LT01_6_count 0.31127 0.15049 2.068 0.0391 *
## category_code_LT01_8_count -0.14354 0.26769 -0.536 0.5921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.64
## F-statistic: 177.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.641483726549553
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9560 -0.7428 0.0880 0.9226 3.4980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94634 0.08527 116.652 < 2e-16 ***
## category_code_LT01_2_count 0.48752 0.09006 5.413 9.68e-08 ***
## category_code_LT01_4_count 0.63777 0.09240 6.902 1.58e-11 ***
## category_code_LT01_5_count 0.90136 0.06063 14.867 < 2e-16 ***
## category_code_LT01_6_count 0.29836 0.15017 1.987 0.0475 *
## category_code_LT01_9_count 0.33877 0.22008 1.539 0.1244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 492 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6415
## F-statistic: 178.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.640077133528101
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9486 -0.7298 0.0891 0.8875 3.4221
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93427 0.08846 112.308 < 2e-16 ***
## category_code_LT01_2_count 0.50105 0.08972 5.585 3.88e-08 ***
## category_code_LT01_4_count 0.64620 0.09239 6.994 8.74e-12 ***
## category_code_LT01_5_count 0.90811 0.06059 14.987 < 2e-16 ***
## category_code_LT01_6_count 0.29291 0.15204 1.927 0.0546 .
## category_code_LT01_10_count 0.07332 0.11087 0.661 0.5087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6401
## F-statistic: 177.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.642263624407467
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9693 -0.7449 0.0519 0.8519 3.4831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95690 0.08524 116.808 < 2e-16 ***
## category_code_LT01_2_count 0.44012 0.09586 4.591 5.60e-06 ***
## category_code_LT01_4_count 0.58816 0.09764 6.024 3.34e-09 ***
## category_code_LT01_5_count 0.90527 0.06043 14.982 < 2e-16 ***
## category_code_LT01_6_count 0.27737 0.15078 1.840 0.0664 .
## category_code_LT01_11_count 0.21665 0.11669 1.857 0.0640 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 492 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6423
## F-statistic: 179.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.639765130016582
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9641 -0.7386 0.0848 0.8937 3.4869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94950 0.08544 116.444 < 2e-16 ***
## category_code_LT01_2_count 0.50635 0.09022 5.613 3.33e-08 ***
## category_code_LT01_4_count 0.64939 0.09256 7.016 7.58e-12 ***
## category_code_LT01_5_count 0.90856 0.06082 14.938 < 2e-16 ***
## category_code_LT01_6_count 0.30948 0.15113 2.048 0.0411 *
## category_code_LT01_12_count -0.02117 0.20263 -0.105 0.9168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6398
## F-statistic: 177.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.639838766570435
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9633 -0.7382 0.0801 0.8854 3.4881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94942 0.08544 116.454 < 2e-16 ***
## category_code_LT01_2_count 0.50362 0.08965 5.617 3.25e-08 ***
## category_code_LT01_4_count 0.64557 0.09282 6.955 1.13e-11 ***
## category_code_LT01_5_count 0.90750 0.06063 14.967 < 2e-16 ***
## category_code_LT01_6_count 0.30898 0.15042 2.054 0.0405 *
## category_code_LT01_13_count 0.07936 0.23766 0.334 0.7386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6398
## F-statistic: 177.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.639838451272805
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9634 -0.7388 0.0873 0.8919 3.4888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95067 0.08551 116.370 < 2e-16 ***
## category_code_LT01_2_count 0.50291 0.08979 5.601 3.55e-08 ***
## category_code_LT01_4_count 0.64381 0.09352 6.884 1.78e-11 ***
## category_code_LT01_5_count 0.90578 0.06099 14.852 < 2e-16 ***
## category_code_LT01_6_count 0.31309 0.15118 2.071 0.0389 *
## category_code_LT01_14_count 0.10719 0.32162 0.333 0.7391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6398
## F-statistic: 177.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.639758835924949
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.7384 0.0858 0.8948 3.4876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94946 0.08545 116.439 < 2e-16 ***
## category_code_LT01_2_count 0.50543 0.08968 5.636 2.93e-08 ***
## category_code_LT01_4_count 0.64909 0.09262 7.008 8.01e-12 ***
## category_code_LT01_5_count 0.90798 0.06063 14.975 < 2e-16 ***
## category_code_LT01_6_count 0.30821 0.15052 2.048 0.0411 *
## category_code_LT01_15_count -0.03533 0.73278 -0.048 0.9616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6398
## F-statistic: 177.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.639986643022393
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9633 -0.7394 0.0786 0.9035 3.4884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94980 0.08542 116.481 < 2e-16 ***
## category_code_LT01_2_count 0.49821 0.09037 5.513 5.71e-08 ***
## category_code_LT01_4_count 0.64951 0.09233 7.035 6.72e-12 ***
## category_code_LT01_5_count 0.90691 0.06063 14.958 < 2e-16 ***
## category_code_LT01_6_count 0.31574 0.15100 2.091 0.037 *
## category_code_LT01_16_count 0.64254 1.14730 0.560 0.576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.64
## F-statistic: 177.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.642021399910987
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9830 -0.7136 0.0509 0.8561 3.4644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96206 0.08511 117.053 < 2e-16 ***
## category_code_LT01_2_count 0.51514 0.08761 5.880 7.58e-09 ***
## category_code_LT01_4_count 0.62604 0.09311 6.723 4.94e-11 ***
## category_code_LT01_5_count 0.91760 0.06086 15.078 < 2e-16 ***
## category_code_LT01_7_count 0.40281 0.15090 2.669 0.00785 **
## category_code_LT01_8_count -0.14417 0.26684 -0.540 0.58924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 492 degrees of freedom
## Multiple R-squared: 0.6456, Adjusted R-squared: 0.642
## F-statistic: 179.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.643206808722967
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9707 -0.7114 0.0338 0.8941 3.4811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95739 0.08495 117.210 < 2e-16 ***
## category_code_LT01_2_count 0.50000 0.08817 5.671 2.43e-08 ***
## category_code_LT01_4_count 0.61729 0.09313 6.629 8.93e-11 ***
## category_code_LT01_5_count 0.90669 0.06023 15.054 < 2e-16 ***
## category_code_LT01_7_count 0.38081 0.15121 2.518 0.0121 *
## category_code_LT01_9_count 0.30585 0.22030 1.388 0.1657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 492 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6432
## F-statistic: 180.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.642240062291799
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9597 -0.7167 0.0443 0.8658 3.3972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94251 0.08822 112.704 < 2e-16 ***
## category_code_LT01_2_count 0.50907 0.08798 5.786 1.28e-08 ***
## category_code_LT01_4_count 0.62148 0.09321 6.668 6.99e-11 ***
## category_code_LT01_5_count 0.91220 0.06016 15.163 < 2e-16 ***
## category_code_LT01_7_count 0.39157 0.15118 2.590 0.00988 **
## category_code_LT01_10_count 0.08438 0.10959 0.770 0.44171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 492 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6422
## F-statistic: 179.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.643483529402165
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7141 0.0214 0.8651 3.4686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96575 0.08496 117.303 < 2e-16 ***
## category_code_LT01_2_count 0.46207 0.09424 4.903 1.28e-06 ***
## category_code_LT01_4_count 0.57972 0.09763 5.938 5.45e-09 ***
## category_code_LT01_5_count 0.91039 0.06007 15.156 < 2e-16 ***
## category_code_LT01_7_count 0.34792 0.15438 2.254 0.0247 *
## category_code_LT01_11_count 0.18054 0.11877 1.520 0.1291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 492 degrees of freedom
## Multiple R-squared: 0.6471, Adjusted R-squared: 0.6435
## F-statistic: 180.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.64181720582343
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9776 -0.7052 0.0646 0.8639 3.4720
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96053 0.08509 117.061 < 2e-16 ***
## category_code_LT01_2_count 0.51419 0.08860 5.804 1.16e-08 ***
## category_code_LT01_4_count 0.62441 0.09348 6.679 6.51e-11 ***
## category_code_LT01_5_count 0.91208 0.06045 15.088 < 2e-16 ***
## category_code_LT01_7_count 0.40016 0.15086 2.653 0.00825 **
## category_code_LT01_12_count 0.02135 0.20107 0.106 0.91549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 492 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6418
## F-statistic: 179.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.641809083437371
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7038 0.0600 0.8610 3.4713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.960583 0.085088 117.061 < 2e-16 ***
## category_code_LT01_2_count 0.515606 0.087675 5.881 7.54e-09 ***
## category_code_LT01_4_count 0.625340 0.093351 6.699 5.76e-11 ***
## category_code_LT01_5_count 0.912691 0.060202 15.161 < 2e-16 ***
## category_code_LT01_7_count 0.400297 0.151849 2.636 0.00865 **
## category_code_LT01_13_count -0.002557 0.238509 -0.011 0.99145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 492 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6418
## F-statistic: 179.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.641810589010567
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7057 0.0587 0.8588 3.4712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96040 0.08518 116.936 < 2e-16 ***
## category_code_LT01_2_count 0.51575 0.08771 5.880 7.58e-09 ***
## category_code_LT01_4_count 0.62580 0.09381 6.671 6.85e-11 ***
## category_code_LT01_5_count 0.91295 0.06048 15.096 < 2e-16 ***
## category_code_LT01_7_count 0.40056 0.15117 2.650 0.00831 **
## category_code_LT01_14_count -0.01494 0.31969 -0.047 0.96275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 492 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6418
## F-statistic: 179.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.641820700078447
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9782 -0.7027 0.0547 0.8617 3.4711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96064 0.08509 117.063 < 2e-16 ***
## category_code_LT01_2_count 0.51479 0.08785 5.860 8.49e-09 ***
## category_code_LT01_4_count 0.62417 0.09354 6.673 6.77e-11 ***
## category_code_LT01_5_count 0.91279 0.06020 15.163 < 2e-16 ***
## category_code_LT01_7_count 0.40078 0.15095 2.655 0.00819 **
## category_code_LT01_15_count 0.09261 0.73053 0.127 0.89917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 492 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6418
## F-statistic: 179.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.64192862433659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7003 0.0583 0.8607 3.4715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96097 0.08508 117.078 < 2e-16 ***
## category_code_LT01_2_count 0.51128 0.08825 5.793 1.23e-08 ***
## category_code_LT01_4_count 0.62640 0.09316 6.724 4.91e-11 ***
## category_code_LT01_5_count 0.91209 0.06020 15.151 < 2e-16 ***
## category_code_LT01_7_count 0.40095 0.15085 2.658 0.00812 **
## category_code_LT01_16_count 0.46194 1.13940 0.405 0.68534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 492 degrees of freedom
## Multiple R-squared: 0.6455, Adjusted R-squared: 0.6419
## F-statistic: 179.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.638795768194382
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9778 -0.7548 0.0611 0.8692 3.4693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95637 0.08551 116.432 < 2e-16 ***
## category_code_LT01_2_count 0.52823 0.08798 6.004 3.74e-09 ***
## category_code_LT01_4_count 0.67616 0.09082 7.445 4.38e-13 ***
## category_code_LT01_5_count 0.91830 0.06121 15.002 < 2e-16 ***
## category_code_LT01_8_count -0.13584 0.26806 -0.507 0.613
## category_code_LT01_9_count 0.36077 0.22085 1.634 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 492 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.6388
## F-statistic: 176.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.63752617236991
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9630 -0.7482 0.0592 0.8483 3.3645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93719 0.08879 111.918 < 2e-16 ***
## category_code_LT01_2_count 0.53941 0.08776 6.147 1.64e-09 ***
## category_code_LT01_4_count 0.68259 0.09088 7.511 2.79e-13 ***
## category_code_LT01_5_count 0.92470 0.06117 15.116 < 2e-16 ***
## category_code_LT01_8_count -0.12674 0.26844 -0.472 0.637
## category_code_LT01_10_count 0.10645 0.11004 0.967 0.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 492 degrees of freedom
## Multiple R-squared: 0.6412, Adjusted R-squared: 0.6375
## F-statistic: 175.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.639926304185589
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7513 0.0797 0.8509 3.4564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96695 0.08542 116.680 < 2e-16 ***
## category_code_LT01_2_count 0.47194 0.09461 4.988 8.47e-07 ***
## category_code_LT01_4_count 0.61775 0.09677 6.384 4.00e-10 ***
## category_code_LT01_5_count 0.92045 0.06101 15.086 < 2e-16 ***
## category_code_LT01_8_count -0.10982 0.26752 -0.411 0.6816
## category_code_LT01_11_count 0.23914 0.11639 2.055 0.0404 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6399
## F-statistic: 177.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.636846392392106
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7494 0.0513 0.8370 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95983 0.08572 116.194 < 2e-16 ***
## category_code_LT01_2_count 0.54701 0.08832 6.194 1.24e-09 ***
## category_code_LT01_4_count 0.68809 0.09108 7.555 2.06e-13 ***
## category_code_LT01_5_count 0.92471 0.06145 15.047 < 2e-16 ***
## category_code_LT01_8_count -0.12173 0.26881 -0.453 0.651
## category_code_LT01_12_count 0.02318 0.20260 0.114 0.909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6368
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.636888134796744
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7494 0.0410 0.8447 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95980 0.08571 116.201 < 2e-16 ***
## category_code_LT01_2_count 0.54739 0.08743 6.261 8.35e-10 ***
## category_code_LT01_4_count 0.68657 0.09119 7.529 2.46e-13 ***
## category_code_LT01_5_count 0.92478 0.06126 15.097 < 2e-16 ***
## category_code_LT01_8_count -0.11647 0.26904 -0.433 0.665
## category_code_LT01_13_count 0.06307 0.23898 0.264 0.792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6369
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.636848779709132
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7500 0.0502 0.8351 3.4584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96039 0.08581 116.074 < 2e-16 ***
## category_code_LT01_2_count 0.54792 0.08746 6.265 8.15e-10 ***
## category_code_LT01_4_count 0.68738 0.09162 7.502 2.95e-13 ***
## category_code_LT01_5_count 0.92455 0.06152 15.029 < 2e-16 ***
## category_code_LT01_8_count -0.12107 0.26864 -0.451 0.652
## category_code_LT01_14_count 0.04105 0.32129 0.128 0.898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6368
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.636837873098915
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7495 0.0485 0.8441 3.4580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95989 0.08572 116.193 < 2e-16 ***
## category_code_LT01_2_count 0.54828 0.08753 6.264 8.20e-10 ***
## category_code_LT01_4_count 0.68870 0.09107 7.562 1.96e-13 ***
## category_code_LT01_5_count 0.92536 0.06124 15.111 < 2e-16 ***
## category_code_LT01_8_count -0.12068 0.26864 -0.449 0.653
## category_code_LT01_15_count 0.02896 0.73520 0.039 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6368
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.636949021526785
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7499 0.0409 0.8419 3.4581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96031 0.08571 116.208 < 2e-16 ***
## category_code_LT01_2_count 0.54441 0.08795 6.190 1.27e-09 ***
## category_code_LT01_4_count 0.69028 0.09077 7.605 1.46e-13 ***
## category_code_LT01_5_count 0.92494 0.06123 15.107 < 2e-16 ***
## category_code_LT01_8_count -0.12591 0.26893 -0.468 0.640
## category_code_LT01_16_count 0.44810 1.14869 0.390 0.697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6406, Adjusted R-squared: 0.6369
## F-statistic: 175.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.639047085851322
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9550 -0.7309 0.0662 0.8717 3.4000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93694 0.08858 112.183 < 2e-16 ***
## category_code_LT01_2_count 0.52248 0.08831 5.917 6.16e-09 ***
## category_code_LT01_4_count 0.67069 0.09096 7.374 7.08e-13 ***
## category_code_LT01_5_count 0.91345 0.06052 15.095 < 2e-16 ***
## category_code_LT01_9_count 0.33671 0.22217 1.516 0.130
## category_code_LT01_10_count 0.08559 0.11054 0.774 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 492 degrees of freedom
## Multiple R-squared: 0.6427, Adjusted R-squared: 0.639
## F-statistic: 177 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.64148114932466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9777 -0.7518 0.0781 0.8483 3.4728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96219 0.08523 116.887 < 2e-16 ***
## category_code_LT01_2_count 0.45598 0.09498 4.801 2.10e-06 ***
## category_code_LT01_4_count 0.60726 0.09671 6.279 7.49e-10 ***
## category_code_LT01_5_count 0.90983 0.06034 15.078 < 2e-16 ***
## category_code_LT01_9_count 0.33415 0.22019 1.518 0.1298
## category_code_LT01_11_count 0.23091 0.11627 1.986 0.0476 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 492 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6415
## F-statistic: 178.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.638613446670438
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9728 -0.7463 0.0591 0.8645 3.4763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95499 0.08549 116.440 < 2e-16 ***
## category_code_LT01_2_count 0.52746 0.08896 5.929 5.74e-09 ***
## category_code_LT01_4_count 0.67443 0.09118 7.396 6.08e-13 ***
## category_code_LT01_5_count 0.91318 0.06081 15.017 < 2e-16 ***
## category_code_LT01_9_count 0.35679 0.22077 1.616 0.107
## category_code_LT01_12_count 0.01857 0.20196 0.092 0.927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 492 degrees of freedom
## Multiple R-squared: 0.6422, Adjusted R-squared: 0.6386
## F-statistic: 176.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.638721838248256
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9726 -0.7445 0.0600 0.8721 3.4766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95492 0.08548 116.458 < 2e-16 ***
## category_code_LT01_2_count 0.52666 0.08813 5.976 4.40e-09 ***
## category_code_LT01_4_count 0.67137 0.09132 7.352 8.22e-13 ***
## category_code_LT01_5_count 0.91299 0.06057 15.074 < 2e-16 ***
## category_code_LT01_9_count 0.36249 0.22120 1.639 0.102
## category_code_LT01_13_count 0.09421 0.23847 0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 492 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.6387
## F-statistic: 176.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.63860755766467
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9732 -0.7486 0.0576 0.8720 3.4758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.955119 0.085593 116.307 < 2e-16 ***
## category_code_LT01_2_count 0.528576 0.088084 6.001 3.81e-09 ***
## category_code_LT01_4_count 0.674914 0.091657 7.364 7.59e-13 ***
## category_code_LT01_5_count 0.913571 0.060829 15.019 < 2e-16 ***
## category_code_LT01_9_count 0.356572 0.221200 1.612 0.108
## category_code_LT01_14_count 0.006711 0.321092 0.021 0.983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 492 degrees of freedom
## Multiple R-squared: 0.6422, Adjusted R-squared: 0.6386
## F-statistic: 176.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.638611277593956
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9733 -0.7500 0.0580 0.8728 3.4756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95506 0.08549 116.441 < 2e-16 ***
## category_code_LT01_2_count 0.52820 0.08821 5.988 4.10e-09 ***
## category_code_LT01_4_count 0.67457 0.09119 7.398 6.03e-13 ***
## category_code_LT01_5_count 0.91376 0.06056 15.089 < 2e-16 ***
## category_code_LT01_9_count 0.35726 0.22084 1.618 0.106
## category_code_LT01_15_count 0.05441 0.73357 0.074 0.941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 492 degrees of freedom
## Multiple R-squared: 0.6422, Adjusted R-squared: 0.6386
## F-statistic: 176.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.638681483794892
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9733 -0.7477 0.0511 0.8731 3.4758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95536 0.08549 116.449 < 2e-16 ***
## category_code_LT01_2_count 0.52545 0.08857 5.933 5.62e-09 ***
## category_code_LT01_4_count 0.67625 0.09088 7.441 4.49e-13 ***
## category_code_LT01_5_count 0.91329 0.06056 15.081 < 2e-16 ***
## category_code_LT01_9_count 0.35470 0.22085 1.606 0.109
## category_code_LT01_16_count 0.36407 1.14499 0.318 0.751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 492 degrees of freedom
## Multiple R-squared: 0.6423, Adjusted R-squared: 0.6387
## F-statistic: 176.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.640390827215734
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9645 -0.7362 0.0724 0.8524 3.3755
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94475 0.08851 112.355 < 2e-16 ***
## category_code_LT01_2_count 0.46444 0.09491 4.894 1.34e-06 ***
## category_code_LT01_4_count 0.61153 0.09683 6.316 6.02e-10 ***
## category_code_LT01_5_count 0.91594 0.06027 15.197 < 2e-16 ***
## category_code_LT01_10_count 0.09832 0.10963 0.897 0.3703
## category_code_LT01_11_count 0.23685 0.11635 2.036 0.0423 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.6404
## F-statistic: 178 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.637364834444846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9587 -0.7665 0.0673 0.8529 3.3723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93618 0.08878 111.915 < 2e-16 ***
## category_code_LT01_2_count 0.53891 0.08870 6.076 2.48e-09 ***
## category_code_LT01_4_count 0.68112 0.09122 7.467 3.77e-13 ***
## category_code_LT01_5_count 0.91998 0.06074 15.145 < 2e-16 ***
## category_code_LT01_10_count 0.10496 0.11011 0.953 0.341
## category_code_LT01_12_count 0.01266 0.20245 0.063 0.950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 492 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6374
## F-statistic: 175.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.637413388303566
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9588 -0.7660 0.0486 0.8530 3.3728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93629 0.08878 111.923 < 2e-16 ***
## category_code_LT01_2_count 0.53865 0.08786 6.131 1.80e-09 ***
## category_code_LT01_4_count 0.67925 0.09131 7.439 4.56e-13 ***
## category_code_LT01_5_count 0.91995 0.06050 15.206 < 2e-16 ***
## category_code_LT01_10_count 0.10444 0.11007 0.949 0.343
## category_code_LT01_13_count 0.06301 0.23849 0.264 0.792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 492 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6374
## F-statistic: 175.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.637366897669154
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9586 -0.7684 0.0650 0.8519 3.3698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93541 0.08925 111.319 < 2e-16 ***
## category_code_LT01_2_count 0.53993 0.08782 6.148 1.62e-09 ***
## category_code_LT01_4_count 0.68256 0.09162 7.450 4.24e-13 ***
## category_code_LT01_5_count 0.92083 0.06079 15.149 < 2e-16 ***
## category_code_LT01_10_count 0.10716 0.11256 0.952 0.342
## category_code_LT01_14_count -0.02690 0.32837 -0.082 0.935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 492 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6374
## F-statistic: 175.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.637363126434412
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9588 -0.7675 0.0658 0.8524 3.3715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93607 0.08881 111.878 < 2e-16 ***
## category_code_LT01_2_count 0.53991 0.08792 6.141 1.70e-09 ***
## category_code_LT01_4_count 0.68191 0.09119 7.478 3.48e-13 ***
## category_code_LT01_5_count 0.92029 0.06049 15.213 < 2e-16 ***
## category_code_LT01_10_count 0.10556 0.11037 0.956 0.339
## category_code_LT01_15_count -0.02943 0.73682 -0.040 0.968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 492 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6374
## F-statistic: 175.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.637442430433873
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9593 -0.7648 0.0586 0.8531 3.3731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93678 0.08879 111.910 < 2e-16 ***
## category_code_LT01_2_count 0.53636 0.08835 6.071 2.54e-09 ***
## category_code_LT01_4_count 0.68273 0.09093 7.508 2.84e-13 ***
## category_code_LT01_5_count 0.91988 0.06049 15.206 < 2e-16 ***
## category_code_LT01_10_count 0.10382 0.11011 0.943 0.346
## category_code_LT01_16_count 0.37914 1.14726 0.330 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 492 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6374
## F-statistic: 175.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.63992060451897
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9881 -0.7545 0.0772 0.8593 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96640 0.08539 116.714 < 2e-16 ***
## category_code_LT01_2_count 0.47357 0.09471 5.000 7.99e-07 ***
## category_code_LT01_4_count 0.61651 0.09673 6.374 4.25e-10 ***
## category_code_LT01_5_count 0.91875 0.06053 15.178 < 2e-16 ***
## category_code_LT01_11_count 0.25141 0.11976 2.099 0.0363 *
## category_code_LT01_12_count -0.08317 0.20747 -0.401 0.6887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 492 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6399
## F-statistic: 177.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.639832160210731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9859 -0.7504 0.0798 0.8454 3.4620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96580 0.08539 116.707 < 2e-16 ***
## category_code_LT01_2_count 0.47129 0.09466 4.979 8.87e-07 ***
## category_code_LT01_4_count 0.61505 0.09703 6.338 5.25e-10 ***
## category_code_LT01_5_count 0.91637 0.06033 15.190 < 2e-16 ***
## category_code_LT01_11_count 0.23905 0.11650 2.052 0.0407 *
## category_code_LT01_13_count 0.04749 0.23784 0.200 0.8418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6398
## F-statistic: 177.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.639811428512661
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7515 0.0822 0.8571 3.4619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96628 0.08548 116.585 < 2e-16 ***
## category_code_LT01_2_count 0.47134 0.09473 4.976 9.00e-07 ***
## category_code_LT01_4_count 0.61521 0.09755 6.307 6.35e-10 ***
## category_code_LT01_5_count 0.91599 0.06062 15.111 < 2e-16 ***
## category_code_LT01_11_count 0.23999 0.11639 2.062 0.0397 *
## category_code_LT01_14_count 0.03437 0.31994 0.107 0.9145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6398
## F-statistic: 177.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.639805292302482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9860 -0.7534 0.0815 0.8557 3.4617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96584 0.08539 116.703 < 2e-16 ***
## category_code_LT01_2_count 0.47204 0.09472 4.984 8.66e-07 ***
## category_code_LT01_4_count 0.61692 0.09695 6.363 4.53e-10 ***
## category_code_LT01_5_count 0.91658 0.06032 15.194 < 2e-16 ***
## category_code_LT01_11_count 0.24036 0.11650 2.063 0.0396 *
## category_code_LT01_15_count -0.04120 0.73284 -0.056 0.9552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6398
## F-statistic: 177.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.639909418075598
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7514 0.0813 0.8562 3.4619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96623 0.08539 116.718 < 2e-16 ***
## category_code_LT01_2_count 0.46773 0.09522 4.912 1.23e-06 ***
## category_code_LT01_4_count 0.61767 0.09677 6.383 4.03e-10 ***
## category_code_LT01_5_count 0.91610 0.06032 15.187 < 2e-16 ***
## category_code_LT01_11_count 0.24035 0.11637 2.065 0.0394 *
## category_code_LT01_16_count 0.43571 1.14253 0.381 0.7031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 492 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6399
## F-statistic: 177.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.636755891061432
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9813 -0.7481 0.0448 0.8388 3.4650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95857 0.08568 116.223 < 2e-16 ***
## category_code_LT01_2_count 0.54628 0.08841 6.179 1.36e-09 ***
## category_code_LT01_4_count 0.68466 0.09151 7.482 3.40e-13 ***
## category_code_LT01_5_count 0.92022 0.06081 15.134 < 2e-16 ***
## category_code_LT01_12_count 0.01838 0.20253 0.091 0.928
## category_code_LT01_13_count 0.06853 0.23869 0.287 0.774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6368
## F-statistic: 175.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.636704732188782
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7486 0.0489 0.8368 3.4648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95906 0.08579 116.093 < 2e-16 ***
## category_code_LT01_2_count 0.54699 0.08841 6.187 1.29e-09 ***
## category_code_LT01_4_count 0.68582 0.09190 7.462 3.88e-13 ***
## category_code_LT01_5_count 0.91995 0.06106 15.065 < 2e-16 ***
## category_code_LT01_12_count 0.01809 0.20298 0.089 0.929
## category_code_LT01_14_count 0.03691 0.32208 0.115 0.909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6367
## F-statistic: 175.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.636696009452769
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7482 0.0426 0.8474 3.4645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95862 0.08569 116.213 < 2e-16 ***
## category_code_LT01_2_count 0.54719 0.08854 6.180 1.35e-09 ***
## category_code_LT01_4_count 0.68693 0.09144 7.512 2.76e-13 ***
## category_code_LT01_5_count 0.92064 0.06080 15.141 < 2e-16 ***
## category_code_LT01_12_count 0.01986 0.20255 0.098 0.922
## category_code_LT01_15_count 0.02673 0.73548 0.036 0.971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6367
## F-statistic: 175.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.636795138625374
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7485 0.0422 0.8459 3.4648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95895 0.08569 116.227 < 2e-16 ***
## category_code_LT01_2_count 0.54347 0.08897 6.109 2.04e-09 ***
## category_code_LT01_4_count 0.68833 0.09112 7.554 2.07e-13 ***
## category_code_LT01_5_count 0.92005 0.06081 15.131 < 2e-16 ***
## category_code_LT01_12_count 0.02092 0.20250 0.103 0.918
## category_code_LT01_16_count 0.42259 1.14759 0.368 0.713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6368
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.636760826089038
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7487 0.0452 0.8367 3.4647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95910 0.08578 116.107 < 2e-16 ***
## category_code_LT01_2_count 0.54689 0.08757 6.245 9.17e-10 ***
## category_code_LT01_4_count 0.68380 0.09207 7.427 4.93e-13 ***
## category_code_LT01_5_count 0.91998 0.06085 15.118 < 2e-16 ***
## category_code_LT01_13_count 0.06913 0.23862 0.290 0.772
## category_code_LT01_14_count 0.03924 0.32129 0.122 0.903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6368
## F-statistic: 175.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.636751895140679
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7482 0.0377 0.8457 3.4643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95863 0.08568 116.224 < 2e-16 ***
## category_code_LT01_2_count 0.54713 0.08766 6.241 9.37e-10 ***
## category_code_LT01_4_count 0.68492 0.09157 7.480 3.45e-13 ***
## category_code_LT01_5_count 0.92078 0.06055 15.206 < 2e-16 ***
## category_code_LT01_13_count 0.06985 0.23914 0.292 0.770
## category_code_LT01_15_count 0.03913 0.73681 0.053 0.958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6368
## F-statistic: 175.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.636855433458738
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7485 0.0373 0.8484 3.4647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95897 0.08568 116.239 < 2e-16 ***
## category_code_LT01_2_count 0.54342 0.08808 6.169 1.43e-09 ***
## category_code_LT01_4_count 0.68643 0.09120 7.527 2.50e-13 ***
## category_code_LT01_5_count 0.92017 0.06056 15.195 < 2e-16 ***
## category_code_LT01_13_count 0.07257 0.23878 0.304 0.761
## category_code_LT01_16_count 0.43435 1.14822 0.378 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6369
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.636699677311834
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9820 -0.7487 0.0429 0.8364 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95915 0.08578 116.096 < 2e-16 ***
## category_code_LT01_2_count 0.54794 0.08767 6.250 8.89e-10 ***
## category_code_LT01_4_count 0.68620 0.09195 7.463 3.88e-13 ***
## category_code_LT01_5_count 0.92045 0.06085 15.127 < 2e-16 ***
## category_code_LT01_14_count 0.03880 0.32133 0.121 0.904
## category_code_LT01_15_count 0.02436 0.73531 0.033 0.974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6367
## F-statistic: 175.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.636802010767069
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9820 -0.7491 0.0426 0.8372 3.4645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95957 0.08578 116.108 < 2e-16 ***
## category_code_LT01_2_count 0.54411 0.08812 6.174 1.39e-09 ***
## category_code_LT01_4_count 0.68737 0.09164 7.501 2.98e-13 ***
## category_code_LT01_5_count 0.91976 0.06086 15.113 < 2e-16 ***
## category_code_LT01_14_count 0.04548 0.32175 0.141 0.888
## category_code_LT01_16_count 0.42954 1.14915 0.374 0.709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6368
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.636788893974258
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9821 -0.7486 0.0334 0.8452 3.4641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95902 0.08569 116.227 < 2e-16 ***
## category_code_LT01_2_count 0.54456 0.08819 6.175 1.39e-09 ***
## category_code_LT01_4_count 0.68880 0.09110 7.561 1.97e-13 ***
## category_code_LT01_5_count 0.92068 0.06055 15.205 < 2e-16 ***
## category_code_LT01_15_count 0.03459 0.73565 0.047 0.963
## category_code_LT01_16_count 0.42254 1.14815 0.368 0.713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 492 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.6368
## F-statistic: 175.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.617769632709485
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0283 -0.7691 -0.0337 0.9746 3.8214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97959 0.08794 113.479 < 2e-16 ***
## category_code_LT01_2_count 0.78934 0.07617 10.363 < 2e-16 ***
## category_code_LT01_5_count 0.95768 0.06257 15.306 < 2e-16 ***
## category_code_LT01_6_count 0.50622 0.15181 3.335 0.000919 ***
## category_code_LT01_7_count 0.64109 0.15095 4.247 2.59e-05 ***
## category_code_LT01_8_count -0.15734 0.27597 -0.570 0.568848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6178
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.619411479758865
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0143 -0.7881 0.0090 0.9884 3.8267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97430 0.08775 113.672 < 2e-16 ***
## category_code_LT01_2_count 0.76917 0.07716 9.968 < 2e-16 ***
## category_code_LT01_5_count 0.94520 0.06193 15.263 < 2e-16 ***
## category_code_LT01_6_count 0.48945 0.15156 3.229 0.00132 **
## category_code_LT01_7_count 0.61216 0.15142 4.043 6.13e-05 ***
## category_code_LT01_9_count 0.35590 0.22743 1.565 0.11825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6232, Adjusted R-squared: 0.6194
## F-statistic: 162.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.617770010844134
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0095 -0.7802 -0.0189 0.9871 3.8365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96441 0.09110 109.376 < 2e-16 ***
## category_code_LT01_2_count 0.78639 0.07644 10.288 < 2e-16 ***
## category_code_LT01_5_count 0.95255 0.06189 15.391 < 2e-16 ***
## category_code_LT01_6_count 0.48852 0.15359 3.181 0.00156 **
## category_code_LT01_7_count 0.63065 0.15139 4.166 3.66e-05 ***
## category_code_LT01_10_count 0.06536 0.11456 0.571 0.56856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6178
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.62391741405055
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0242 -0.7353 0.0059 0.9316 3.8153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98565 0.08723 114.473 < 2e-16 ***
## category_code_LT01_2_count 0.65272 0.08923 7.315 1.05e-12 ***
## category_code_LT01_5_count 0.94301 0.06148 15.339 < 2e-16 ***
## category_code_LT01_6_count 0.42719 0.15266 2.798 0.00534 **
## category_code_LT01_7_count 0.50691 0.15633 3.243 0.00127 **
## category_code_LT01_11_count 0.34090 0.11781 2.894 0.00398 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6239
## F-statistic: 165.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.617583516040926
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0219 -0.7912 -0.0229 0.9782 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97797 0.08792 113.484 < 2e-16 ***
## category_code_LT01_2_count 0.78581 0.07761 10.125 < 2e-16 ***
## category_code_LT01_5_count 0.95077 0.06217 15.293 < 2e-16 ***
## category_code_LT01_6_count 0.49731 0.15269 3.257 0.0012 **
## category_code_LT01_7_count 0.63726 0.15090 4.223 2.87e-05 ***
## category_code_LT01_12_count 0.06089 0.20832 0.292 0.7702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6176
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.617676467917144
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0222 -0.7849 -0.0159 0.9784 3.8232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97778 0.08791 113.494 < 2e-16 ***
## category_code_LT01_2_count 0.78691 0.07650 10.286 < 2e-16 ***
## category_code_LT01_5_count 0.95158 0.06193 15.366 < 2e-16 ***
## category_code_LT01_6_count 0.50274 0.15168 3.314 0.000986 ***
## category_code_LT01_7_count 0.62863 0.15226 4.129 4.29e-05 ***
## category_code_LT01_13_count 0.11133 0.24583 0.453 0.650850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6177
## F-statistic: 161.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.618268334561126
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0206 -0.7824 0.0089 0.9720 3.8200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98097 0.08789 113.557 < 2e-16 ***
## category_code_LT01_2_count 0.77677 0.07731 10.048 < 2e-16 ***
## category_code_LT01_5_count 0.94463 0.06236 15.149 < 2e-16 ***
## category_code_LT01_6_count 0.51352 0.15198 3.379 0.000786 ***
## category_code_LT01_7_count 0.62312 0.15150 4.113 4.58e-05 ***
## category_code_LT01_14_count 0.32330 0.32857 0.984 0.325605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6183
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.617741532273401
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0235 -0.7782 -0.0325 0.9755 3.8227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97822 0.08791 113.511 < 2e-16 ***
## category_code_LT01_2_count 0.78478 0.07681 10.217 < 2e-16 ***
## category_code_LT01_5_count 0.95269 0.06189 15.392 < 2e-16 ***
## category_code_LT01_6_count 0.49766 0.15192 3.276 0.00113 **
## category_code_LT01_7_count 0.63904 0.15087 4.236 2.72e-05 ***
## category_code_LT01_15_count 0.40452 0.75266 0.537 0.59120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6177
## F-statistic: 161.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.617727532695089
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0227 -0.7936 -0.0194 0.9766 3.8226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97839 0.08791 113.508 < 2e-16 ***
## category_code_LT01_2_count 0.78366 0.07717 10.155 < 2e-16 ***
## category_code_LT01_5_count 0.95138 0.06193 15.363 < 2e-16 ***
## category_code_LT01_6_count 0.51006 0.15238 3.347 0.000879 ***
## category_code_LT01_7_count 0.63937 0.15088 4.238 2.7e-05 ***
## category_code_LT01_16_count 0.61529 1.18230 0.520 0.603009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6177
## F-statistic: 161.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.606950726371269
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0343 -0.8250 0.0167 0.9826 3.8246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97636 0.08921 111.831 < 2e-16 ***
## category_code_LT01_2_count 0.87355 0.07386 11.827 < 2e-16 ***
## category_code_LT01_5_count 0.97104 0.06340 15.316 < 2e-16 ***
## category_code_LT01_6_count 0.51271 0.15408 3.327 0.000942 ***
## category_code_LT01_8_count -0.13366 0.27983 -0.478 0.633096
## category_code_LT01_9_count 0.45971 0.22989 2.000 0.046080 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 492 degrees of freedom
## Multiple R-squared: 0.6109, Adjusted R-squared: 0.607
## F-statistic: 154.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.604432919964143
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0237 -0.8061 -0.0140 0.9768 3.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95889 0.09269 107.440 < 2e-16 ***
## category_code_LT01_2_count 0.89846 0.07276 12.347 < 2e-16 ***
## category_code_LT01_5_count 0.98090 0.06340 15.471 < 2e-16 ***
## category_code_LT01_6_count 0.50728 0.15634 3.245 0.00126 **
## category_code_LT01_8_count -0.11920 0.28060 -0.425 0.67116
## category_code_LT01_10_count 0.10656 0.11615 0.918 0.35933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6084, Adjusted R-squared: 0.6044
## F-statistic: 152.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.615972835957091
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0416 -0.8154 0.0316 0.9327 3.8442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99044 0.08818 113.297 < 2e-16 ***
## category_code_LT01_2_count 0.69331 0.08929 7.765 4.77e-14 ***
## category_code_LT01_5_count 0.96118 0.06267 15.336 < 2e-16 ***
## category_code_LT01_6_count 0.42352 0.15443 2.742 0.00632 **
## category_code_LT01_8_count -0.09513 0.27648 -0.344 0.73094
## category_code_LT01_11_count 0.45086 0.11396 3.956 8.73e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 492 degrees of freedom
## Multiple R-squared: 0.6198, Adjusted R-squared: 0.616
## F-statistic: 160.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.603863493579496
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0445 -0.8282 -0.0334 0.9672 3.8199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98105 0.08953 111.486 < 2e-16 ***
## category_code_LT01_2_count 0.90108 0.07391 12.191 < 2e-16 ***
## category_code_LT01_5_count 0.97899 0.06370 15.370 < 2e-16 ***
## category_code_LT01_6_count 0.52383 0.15543 3.370 0.00081 ***
## category_code_LT01_8_count -0.11806 0.28093 -0.420 0.67450
## category_code_LT01_12_count 0.07748 0.21213 0.365 0.71510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 492 degrees of freedom
## Multiple R-squared: 0.6078, Adjusted R-squared: 0.6039
## F-statistic: 152.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.604531036032555
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0430 -0.8160 -0.0369 0.9711 3.8206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98031 0.08946 111.567 < 2e-16 ***
## category_code_LT01_2_count 0.89596 0.07302 12.269 < 2e-16 ***
## category_code_LT01_5_count 0.97784 0.06348 15.404 < 2e-16 ***
## category_code_LT01_6_count 0.52983 0.15431 3.434 0.000646 ***
## category_code_LT01_8_count -0.09934 0.28094 -0.354 0.723783
## category_code_LT01_13_count 0.24361 0.24811 0.982 0.326640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 492 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6045
## F-statistic: 152.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.605296807811043
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0421 -0.8258 -0.0003 0.9409 3.8157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98529 0.08941 111.674 < 2e-16 ***
## category_code_LT01_2_count 0.88382 0.07400 11.944 < 2e-16 ***
## category_code_LT01_5_count 0.96936 0.06389 15.172 < 2e-16 ***
## category_code_LT01_6_count 0.54549 0.15455 3.530 0.000455 ***
## category_code_LT01_8_count -0.12282 0.28031 -0.438 0.661471
## category_code_LT01_14_count 0.46082 0.33252 1.386 0.166429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 492 degrees of freedom
## Multiple R-squared: 0.6093, Adjusted R-squared: 0.6053
## F-statistic: 153.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.603938405423157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0464 -0.8013 -0.0030 0.9700 3.8197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98129 0.08952 111.499 < 2e-16 ***
## category_code_LT01_2_count 0.90211 0.07291 12.374 < 2e-16 ***
## category_code_LT01_5_count 0.98134 0.06345 15.467 < 2e-16 ***
## category_code_LT01_6_count 0.52606 0.15467 3.401 0.000726 ***
## category_code_LT01_8_count -0.11596 0.28075 -0.413 0.679758
## category_code_LT01_15_count 0.36458 0.76610 0.476 0.634366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.6039
## F-statistic: 152.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.603925234732303
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0459 -0.8177 -0.0045 0.9715 3.8194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98151 0.08952 111.496 < 2e-16 ***
## category_code_LT01_2_count 0.90117 0.07327 12.299 < 2e-16 ***
## category_code_LT01_5_count 0.98037 0.06346 15.448 < 2e-16 ***
## category_code_LT01_6_count 0.53736 0.15521 3.462 0.000582 ***
## category_code_LT01_8_count -0.12153 0.28116 -0.432 0.665740
## category_code_LT01_16_count 0.55237 1.20508 0.458 0.646891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.6039
## F-statistic: 152.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.607147594879868
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0135 -0.8254 0.0113 0.9696 3.8425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95849 0.09235 107.838 < 2e-16 ***
## category_code_LT01_2_count 0.86909 0.07418 11.716 < 2e-16 ***
## category_code_LT01_5_count 0.96678 0.06269 15.421 < 2e-16 ***
## category_code_LT01_6_count 0.49283 0.15579 3.164 0.00166 **
## category_code_LT01_9_count 0.43760 0.23123 1.892 0.05901 .
## category_code_LT01_10_count 0.08028 0.11650 0.689 0.49109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 492 degrees of freedom
## Multiple R-squared: 0.6111, Adjusted R-squared: 0.6071
## F-statistic: 154.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.618222070433971
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0279 -0.8152 0.0331 0.9156 3.9159
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98497 0.08792 113.568 < 2e-16 ***
## category_code_LT01_2_count 0.67126 0.08991 7.466 3.78e-13 ***
## category_code_LT01_5_count 0.94938 0.06196 15.322 < 2e-16 ***
## category_code_LT01_6_count 0.40881 0.15396 2.655 0.008181 **
## category_code_LT01_9_count 0.39433 0.22700 1.737 0.082987 .
## category_code_LT01_11_count 0.43758 0.11390 3.842 0.000138 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6182
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.606862454287677
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0284 -0.8376 0.0128 0.9747 3.8260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97498 0.08918 111.851 < 2e-16 ***
## category_code_LT01_2_count 0.86873 0.07546 11.513 < 2e-16 ***
## category_code_LT01_5_count 0.96456 0.06299 15.313 < 2e-16 ***
## category_code_LT01_6_count 0.50343 0.15496 3.249 0.00124 **
## category_code_LT01_9_count 0.45558 0.22978 1.983 0.04796 *
## category_code_LT01_12_count 0.07244 0.21120 0.343 0.73175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 492 degrees of freedom
## Multiple R-squared: 0.6108, Adjusted R-squared: 0.6069
## F-statistic: 154.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.607765522682003
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0267 -0.8214 0.0175 0.9766 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97416 0.08908 111.966 <2e-16 ***
## category_code_LT01_2_count 0.86070 0.07473 11.517 <2e-16 ***
## category_code_LT01_5_count 0.96321 0.06271 15.359 <2e-16 ***
## category_code_LT01_6_count 0.50891 0.15378 3.309 0.0010 **
## category_code_LT01_9_count 0.47014 0.22986 2.045 0.0414 *
## category_code_LT01_13_count 0.27634 0.24710 1.118 0.2640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 492 degrees of freedom
## Multiple R-squared: 0.6117, Adjusted R-squared: 0.6078
## F-statistic: 155 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.607974275387115
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0268 -0.8228 0.0335 0.9600 3.8220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97894 0.08911 111.986 < 2e-16 ***
## category_code_LT01_2_count 0.85527 0.07532 11.356 < 2e-16 ***
## category_code_LT01_5_count 0.95648 0.06316 15.144 < 2e-16 ***
## category_code_LT01_6_count 0.52370 0.15417 3.397 0.000737 ***
## category_code_LT01_9_count 0.43386 0.23015 1.885 0.060007 .
## category_code_LT01_14_count 0.40882 0.33233 1.230 0.219218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 492 degrees of freedom
## Multiple R-squared: 0.6119, Adjusted R-squared: 0.608
## F-statistic: 155.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.606979093771281
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0302 -0.8295 0.0052 0.9794 3.8257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97522 0.08917 111.870 < 2e-16 ***
## category_code_LT01_2_count 0.86883 0.07454 11.656 < 2e-16 ***
## category_code_LT01_5_count 0.96679 0.06270 15.418 < 2e-16 ***
## category_code_LT01_6_count 0.50487 0.15420 3.274 0.00113 **
## category_code_LT01_9_count 0.45833 0.22979 1.995 0.04664 *
## category_code_LT01_15_count 0.39195 0.76327 0.514 0.60782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 492 degrees of freedom
## Multiple R-squared: 0.6109, Adjusted R-squared: 0.607
## F-statistic: 154.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.60687817718527
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0298 -0.8382 0.0093 0.9763 3.8256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97536 0.08918 111.854 < 2e-16 ***
## category_code_LT01_2_count 0.86973 0.07476 11.633 < 2e-16 ***
## category_code_LT01_5_count 0.96591 0.06274 15.396 < 2e-16 ***
## category_code_LT01_6_count 0.51520 0.15472 3.330 0.000934 ***
## category_code_LT01_9_count 0.45308 0.22990 1.971 0.049311 *
## category_code_LT01_16_count 0.44449 1.19944 0.371 0.711109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 492 degrees of freedom
## Multiple R-squared: 0.6108, Adjusted R-squared: 0.6069
## F-statistic: 154.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.616396312757027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0188 -0.8156 0.0340 0.9336 3.9082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97000 0.09130 109.196 < 2e-16 ***
## category_code_LT01_2_count 0.68709 0.08954 7.673 9.07e-14 ***
## category_code_LT01_5_count 0.95777 0.06191 15.469 < 2e-16 ***
## category_code_LT01_6_count 0.40143 0.15603 2.573 0.0104 *
## category_code_LT01_10_count 0.09306 0.11440 0.813 0.4164
## category_code_LT01_11_count 0.44897 0.11393 3.941 9.30e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 492 degrees of freedom
## Multiple R-squared: 0.6203, Adjusted R-squared: 0.6164
## F-statistic: 160.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.604374568766433
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0186 -0.8063 -0.0152 0.9650 3.8430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95800 0.09267 107.455 <2e-16 ***
## category_code_LT01_2_count 0.89367 0.07437 12.017 <2e-16 ***
## category_code_LT01_5_count 0.97489 0.06296 15.484 <2e-16 ***
## category_code_LT01_6_count 0.49885 0.15715 3.174 0.0016 **
## category_code_LT01_10_count 0.10471 0.11617 0.901 0.3679
## category_code_LT01_12_count 0.06961 0.21193 0.328 0.7427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6084, Adjusted R-squared: 0.6044
## F-statistic: 152.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.605047641607269
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0182 -0.8050 -0.0282 0.9846 3.8429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95804 0.09259 107.547 < 2e-16 ***
## category_code_LT01_2_count 0.88828 0.07349 12.087 < 2e-16 ***
## category_code_LT01_5_count 0.97420 0.06269 15.540 < 2e-16 ***
## category_code_LT01_6_count 0.50530 0.15608 3.237 0.00129 **
## category_code_LT01_10_count 0.10180 0.11610 0.877 0.38103
## category_code_LT01_13_count 0.24100 0.24772 0.973 0.33108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 492 degrees of freedom
## Multiple R-squared: 0.609, Adjusted R-squared: 0.605
## F-statistic: 153.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.60543809136388
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0232 -0.8158 -0.0072 0.9540 3.8325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96849 0.09297 107.226 < 2e-16 ***
## category_code_LT01_2_count 0.88092 0.07417 11.877 < 2e-16 ***
## category_code_LT01_5_count 0.96631 0.06321 15.286 < 2e-16 ***
## category_code_LT01_6_count 0.52505 0.15695 3.345 0.000884 ***
## category_code_LT01_10_count 0.07237 0.11926 0.607 0.544258
## category_code_LT01_14_count 0.40935 0.34180 1.198 0.231636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 492 degrees of freedom
## Multiple R-squared: 0.6094, Adjusted R-squared: 0.6054
## F-statistic: 153.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.604419271344644
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0209 -0.8043 -0.0237 0.9788 3.8422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95878 0.09269 107.441 < 2e-16 ***
## category_code_LT01_2_count 0.89501 0.07334 12.204 < 2e-16 ***
## category_code_LT01_5_count 0.97707 0.06268 15.587 < 2e-16 ***
## category_code_LT01_6_count 0.50166 0.15636 3.208 0.00142 **
## category_code_LT01_10_count 0.10212 0.11646 0.877 0.38099
## category_code_LT01_15_count 0.31043 0.76776 0.404 0.68614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6084, Adjusted R-squared: 0.6044
## F-statistic: 152.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.604409157457756
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0201 -0.8073 -0.0258 0.9677 3.8423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95866 0.09269 107.444 < 2e-16 ***
## category_code_LT01_2_count 0.89415 0.07370 12.132 < 2e-16 ***
## category_code_LT01_5_count 0.97608 0.06271 15.564 < 2e-16 ***
## category_code_LT01_6_count 0.51088 0.15707 3.253 0.00122 **
## category_code_LT01_10_count 0.10329 0.11629 0.888 0.37486
## category_code_LT01_16_count 0.46778 1.20417 0.388 0.69784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6084, Adjusted R-squared: 0.6044
## F-statistic: 152.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.616160591783049
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0410 -0.7858 0.0255 0.9407 3.8357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99010 0.08812 113.365 < 2e-16 ***
## category_code_LT01_2_count 0.69470 0.08930 7.779 4.32e-14 ***
## category_code_LT01_5_count 0.96080 0.06212 15.467 < 2e-16 ***
## category_code_LT01_6_count 0.42784 0.15464 2.767 0.00588 **
## category_code_LT01_11_count 0.46819 0.11725 3.993 7.52e-05 ***
## category_code_LT01_12_count -0.12872 0.21479 -0.599 0.54928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 492 degrees of freedom
## Multiple R-squared: 0.62, Adjusted R-squared: 0.6162
## F-statistic: 160.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.616274705379674
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0366 -0.8258 0.0329 0.9381 3.8575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98891 0.08811 113.369 < 2e-16 ***
## category_code_LT01_2_count 0.68837 0.08950 7.691 7.99e-14 ***
## category_code_LT01_5_count 0.95627 0.06197 15.432 < 2e-16 ***
## category_code_LT01_6_count 0.42244 0.15421 2.739 0.006378 **
## category_code_LT01_11_count 0.44522 0.11425 3.897 0.000111 ***
## category_code_LT01_13_count 0.17403 0.24477 0.711 0.477421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 492 degrees of freedom
## Multiple R-squared: 0.6201, Adjusted R-squared: 0.6163
## F-statistic: 160.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.616862255612012
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0351 -0.8102 0.0328 0.9404 3.8557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99260 0.08808 113.449 < 2e-16 ***
## category_code_LT01_2_count 0.67901 0.09006 7.539 2.29e-13 ***
## category_code_LT01_5_count 0.94873 0.06241 15.201 < 2e-16 ***
## category_code_LT01_6_count 0.43511 0.15459 2.815 0.005079 **
## category_code_LT01_11_count 0.44260 0.11409 3.879 0.000119 ***
## category_code_LT01_14_count 0.36868 0.32834 1.123 0.262049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6169
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.615921349103226
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0386 -0.8269 0.0289 0.9346 3.8542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98956 0.08815 113.330 < 2e-16 ***
## category_code_LT01_2_count 0.69169 0.08951 7.728 6.21e-14 ***
## category_code_LT01_5_count 0.95811 0.06196 15.464 < 2e-16 ***
## category_code_LT01_6_count 0.41936 0.15443 2.716 0.00685 **
## category_code_LT01_11_count 0.44991 0.11418 3.940 9.32e-05 ***
## category_code_LT01_15_count 0.17305 0.75588 0.229 0.81901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 492 degrees of freedom
## Multiple R-squared: 0.6198, Adjusted R-squared: 0.6159
## F-statistic: 160.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.616036774570058
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0381 -0.8269 0.0310 0.9366 3.8508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98982 0.08813 113.347 < 2e-16 ***
## category_code_LT01_2_count 0.68772 0.09009 7.634 1.19e-13 ***
## category_code_LT01_5_count 0.95705 0.06197 15.443 < 2e-16 ***
## category_code_LT01_6_count 0.42762 0.15496 2.760 0.006 **
## category_code_LT01_11_count 0.45163 0.11394 3.964 8.47e-05 ***
## category_code_LT01_16_count 0.53026 1.18472 0.448 0.655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 492 degrees of freedom
## Multiple R-squared: 0.6199, Adjusted R-squared: 0.616
## F-statistic: 160.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.604512595685052
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0382 -0.8269 -0.0295 0.9678 3.8217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97922 0.08942 111.605 < 2e-16 ***
## category_code_LT01_2_count 0.89103 0.07460 11.945 < 2e-16 ***
## category_code_LT01_5_count 0.97253 0.06300 15.436 < 2e-16 ***
## category_code_LT01_6_count 0.52166 0.15517 3.362 0.000835 ***
## category_code_LT01_12_count 0.06772 0.21193 0.320 0.749467
## category_code_LT01_13_count 0.24592 0.24786 0.992 0.321604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 492 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6045
## F-statistic: 152.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.605185008585264
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0370 -0.8312 -0.0101 0.9463 3.8170
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98392 0.08939 111.694 < 2e-16 ***
## category_code_LT01_2_count 0.88092 0.07534 11.693 < 2e-16 ***
## category_code_LT01_5_count 0.96407 0.06342 15.201 < 2e-16 ***
## category_code_LT01_6_count 0.53799 0.15554 3.459 0.00059 ***
## category_code_LT01_12_count 0.04874 0.21250 0.229 0.81869
## category_code_LT01_14_count 0.45085 0.33382 1.351 0.17746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 492 degrees of freedom
## Multiple R-squared: 0.6092, Adjusted R-squared: 0.6052
## F-statistic: 153.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.603906006509694
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0409 -0.8246 -0.0342 0.9662 3.8209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98004 0.08948 111.532 < 2e-16 ***
## category_code_LT01_2_count 0.89663 0.07460 12.020 < 2e-16 ***
## category_code_LT01_5_count 0.97525 0.06300 15.480 < 2e-16 ***
## category_code_LT01_6_count 0.51669 0.15556 3.321 0.000962 ***
## category_code_LT01_12_count 0.07655 0.21204 0.361 0.718231
## category_code_LT01_15_count 0.36703 0.76627 0.479 0.632164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.6039
## F-statistic: 152.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.603875899386529
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0403 -0.8283 -0.0359 0.9645 3.8208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98018 0.08949 111.527 < 2e-16 ***
## category_code_LT01_2_count 0.89609 0.07493 11.960 < 2e-16 ***
## category_code_LT01_5_count 0.97417 0.06303 15.454 < 2e-16 ***
## category_code_LT01_6_count 0.52769 0.15602 3.382 0.000776 ***
## category_code_LT01_12_count 0.07512 0.21201 0.354 0.723241
## category_code_LT01_16_count 0.52731 1.20338 0.438 0.661439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 492 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.6039
## F-statistic: 152.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.605928468937953
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0355 -0.8125 -0.0098 0.9451 3.8176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98334 0.08930 111.792 < 2e-16 ***
## category_code_LT01_2_count 0.87343 0.07472 11.689 < 2e-16 ***
## category_code_LT01_5_count 0.96265 0.06321 15.231 < 2e-16 ***
## category_code_LT01_6_count 0.54216 0.15425 3.515 0.000481 ***
## category_code_LT01_13_count 0.24494 0.24731 0.990 0.322460
## category_code_LT01_14_count 0.45429 0.33220 1.368 0.172078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.414 on 492 degrees of freedom
## Multiple R-squared: 0.6099, Adjusted R-squared: 0.6059
## F-statistic: 153.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.604655230302134
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0399 -0.8146 -0.0364 0.9728 3.8215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97946 0.08940 111.628 < 2e-16 ***
## category_code_LT01_2_count 0.89038 0.07375 12.072 < 2e-16 ***
## category_code_LT01_5_count 0.97460 0.06272 15.539 < 2e-16 ***
## category_code_LT01_6_count 0.52264 0.15438 3.386 0.000767 ***
## category_code_LT01_13_count 0.25578 0.24809 1.031 0.303042
## category_code_LT01_15_count 0.40537 0.76657 0.529 0.597178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 492 degrees of freedom
## Multiple R-squared: 0.6086, Adjusted R-squared: 0.6047
## F-statistic: 153 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.604614866240023
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0392 -0.8227 -0.0362 0.9730 3.8213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97962 0.08941 111.622 < 2e-16 ***
## category_code_LT01_2_count 0.88983 0.07411 12.008 < 2e-16 ***
## category_code_LT01_5_count 0.97340 0.06276 15.510 < 2e-16 ***
## category_code_LT01_6_count 0.53458 0.15487 3.452 0.000605 ***
## category_code_LT01_13_count 0.25349 0.24793 1.022 0.307089
## category_code_LT01_16_count 0.57631 1.20331 0.479 0.632196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 492 degrees of freedom
## Multiple R-squared: 0.6086, Adjusted R-squared: 0.6046
## F-statistic: 153 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.605297254200266
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0383 -0.8191 0.0009 0.9456 3.8168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98416 0.08937 111.715 < 2e-16 ***
## category_code_LT01_2_count 0.88004 0.07458 11.799 < 2e-16 ***
## category_code_LT01_5_count 0.96559 0.06321 15.277 < 2e-16 ***
## category_code_LT01_6_count 0.53830 0.15465 3.481 0.000544 ***
## category_code_LT01_14_count 0.45414 0.33254 1.366 0.172672
## category_code_LT01_15_count 0.33565 0.76497 0.439 0.661011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 492 degrees of freedom
## Multiple R-squared: 0.6093, Adjusted R-squared: 0.6053
## F-statistic: 153.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.605366984040104
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0375 -0.8211 -0.0095 0.9404 3.8164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98451 0.08937 111.724 < 2e-16 ***
## category_code_LT01_2_count 0.87714 0.07517 11.669 < 2e-16 ***
## category_code_LT01_5_count 0.96392 0.06325 15.241 < 2e-16 ***
## category_code_LT01_6_count 0.55068 0.15518 3.549 0.000424 ***
## category_code_LT01_14_count 0.46938 0.33315 1.409 0.159496
## category_code_LT01_16_count 0.63638 1.20372 0.529 0.597268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 492 degrees of freedom
## Multiple R-squared: 0.6093, Adjusted R-squared: 0.6054
## F-statistic: 153.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.603964735022997
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0422 -0.8238 -0.0349 0.9713 3.8205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98046 0.08948 111.543 < 2e-16 ***
## category_code_LT01_2_count 0.89669 0.07397 12.123 < 2e-16 ***
## category_code_LT01_5_count 0.97651 0.06275 15.562 < 2e-16 ***
## category_code_LT01_6_count 0.52988 0.15522 3.414 0.000694 ***
## category_code_LT01_15_count 0.37229 0.76644 0.486 0.627369
## category_code_LT01_16_count 0.54283 1.20384 0.451 0.652252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 492 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.604
## F-statistic: 152.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.611523187718746
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0553 -0.7933 -0.0063 0.9593 3.8076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99337 0.08852 112.890 < 2e-16 ***
## category_code_LT01_2_count 0.87530 0.07051 12.415 < 2e-16 ***
## category_code_LT01_5_count 0.97677 0.06271 15.575 < 2e-16 ***
## category_code_LT01_7_count 0.63067 0.15299 4.122 4.4e-05 ***
## category_code_LT01_8_count -0.13253 0.27809 -0.477 0.6339
## category_code_LT01_9_count 0.39956 0.22956 1.741 0.0824 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6154, Adjusted R-squared: 0.6115
## F-statistic: 157.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.610066268849167
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0381 -0.7912 0.0031 0.9267 3.8298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97113 0.09203 108.347 < 2e-16 ***
## category_code_LT01_2_count 0.88907 0.06997 12.707 < 2e-16 ***
## category_code_LT01_5_count 0.98411 0.06265 15.708 < 2e-16 ***
## category_code_LT01_7_count 0.64511 0.15296 4.218 2.94e-05 ***
## category_code_LT01_8_count -0.12333 0.27853 -0.443 0.658
## category_code_LT01_10_count 0.12414 0.11428 1.086 0.278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6101
## F-statistic: 156.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.618026915985271
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0595 -0.7507 0.0137 0.9213 3.7976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00332 0.08775 113.993 < 2e-16 ***
## category_code_LT01_2_count 0.72291 0.08635 8.372 5.94e-16 ***
## category_code_LT01_5_count 0.96853 0.06220 15.572 < 2e-16 ***
## category_code_LT01_7_count 0.50377 0.15768 3.195 0.001489 **
## category_code_LT01_8_count -0.09649 0.27567 -0.350 0.726486
## category_code_LT01_11_count 0.39613 0.11703 3.385 0.000769 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.609495199102157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0621 -0.7869 -0.0263 0.9379 3.8036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99738 0.08872 112.686 < 2e-16 ***
## category_code_LT01_2_count 0.88932 0.07151 12.436 < 2e-16 ***
## category_code_LT01_5_count 0.98086 0.06301 15.566 < 2e-16 ***
## category_code_LT01_7_count 0.65797 0.15249 4.315 1.93e-05 ***
## category_code_LT01_8_count -0.12404 0.27888 -0.445 0.657
## category_code_LT01_12_count 0.14178 0.20931 0.677 0.499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6095
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.609262289710318
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0647 -0.7787 -0.0171 0.9365 3.8033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99766 0.08874 112.657 < 2e-16 ***
## category_code_LT01_2_count 0.89920 0.06937 12.962 < 2e-16 ***
## category_code_LT01_5_count 0.98418 0.06276 15.682 < 2e-16 ***
## category_code_LT01_7_count 0.65124 0.15397 4.230 2.79e-05 ***
## category_code_LT01_8_count -0.10974 0.27926 -0.393 0.695
## category_code_LT01_13_count 0.10121 0.24898 0.407 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6132, Adjusted R-squared: 0.6093
## F-statistic: 156 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.609556819410415
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0646 -0.7901 -0.0114 0.9161 3.8005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00050 0.08877 112.652 < 2e-16 ***
## category_code_LT01_2_count 0.89390 0.06988 12.792 < 2e-16 ***
## category_code_LT01_5_count 0.97990 0.06310 15.530 < 2e-16 ***
## category_code_LT01_7_count 0.64910 0.15316 4.238 2.69e-05 ***
## category_code_LT01_8_count -0.11959 0.27867 -0.429 0.668
## category_code_LT01_14_count 0.24275 0.33141 0.732 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6096
## F-statistic: 156.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.6095499533859
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.066 -0.776 -0.020 0.930 3.803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99797 0.08871 112.707 < 2e-16 ***
## category_code_LT01_2_count 0.89331 0.07003 12.756 < 2e-16 ***
## category_code_LT01_5_count 0.98525 0.06269 15.717 < 2e-16 ***
## category_code_LT01_7_count 0.66116 0.15247 4.336 1.76e-05 ***
## category_code_LT01_8_count -0.11950 0.27868 -0.429 0.668
## category_code_LT01_15_count 0.55180 0.75948 0.727 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6095
## F-statistic: 156.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.609168318948161
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0659 -0.7872 -0.0196 0.9360 3.8027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99827 0.08876 112.642 < 2e-16 ***
## category_code_LT01_2_count 0.90008 0.06963 12.926 < 2e-16 ***
## category_code_LT01_5_count 0.98503 0.06272 15.705 < 2e-16 ***
## category_code_LT01_7_count 0.66063 0.15257 4.330 1.81e-05 ***
## category_code_LT01_8_count -0.11969 0.27915 -0.429 0.668
## category_code_LT01_16_count 0.25808 1.19140 0.217 0.829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6092
## F-statistic: 155.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.611966072832294
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0286 -0.7727 0.0237 0.9463 3.8308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97020 0.09177 108.639 < 2e-16 ***
## category_code_LT01_2_count 0.86639 0.07121 12.167 < 2e-16 ***
## category_code_LT01_5_count 0.97171 0.06196 15.683 < 2e-16 ***
## category_code_LT01_7_count 0.61768 0.15327 4.030 6.46e-05 ***
## category_code_LT01_9_count 0.37258 0.23081 1.614 0.107
## category_code_LT01_10_count 0.10190 0.11472 0.888 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 492 degrees of freedom
## Multiple R-squared: 0.6159, Adjusted R-squared: 0.612
## F-statistic: 157.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.619854300351212
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0465 -0.7875 0.0131 0.9153 3.8031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99790 0.08754 114.208 < 2e-16 ***
## category_code_LT01_2_count 0.70260 0.08705 8.071 5.4e-15 ***
## category_code_LT01_5_count 0.95748 0.06149 15.572 < 2e-16 ***
## category_code_LT01_7_count 0.47854 0.15783 3.032 0.002557 **
## category_code_LT01_9_count 0.35845 0.22724 1.577 0.115348
## category_code_LT01_11_count 0.38785 0.11686 3.319 0.000971 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6199
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.611673815473372
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0472 -0.7809 0.0057 0.9307 3.8096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99139 0.08847 112.941 < 2e-16 ***
## category_code_LT01_2_count 0.86346 0.07291 11.842 < 2e-16 ***
## category_code_LT01_5_count 0.96793 0.06234 15.527 < 2e-16 ***
## category_code_LT01_7_count 0.62633 0.15290 4.096 4.91e-05 ***
## category_code_LT01_9_count 0.39461 0.22940 1.720 0.086 .
## category_code_LT01_12_count 0.13485 0.20857 0.647 0.518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6156, Adjusted R-squared: 0.6117
## F-statistic: 157.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.611588481786643
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0495 -0.7811 0.0081 0.9544 3.8094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99157 0.08847 112.933 < 2e-16 ***
## category_code_LT01_2_count 0.87095 0.07097 12.272 < 2e-16 ***
## category_code_LT01_5_count 0.97101 0.06203 15.654 < 2e-16 ***
## category_code_LT01_7_count 0.61593 0.15446 3.988 7.68e-05 ***
## category_code_LT01_9_count 0.40507 0.23000 1.761 0.0788 .
## category_code_LT01_13_count 0.13828 0.24841 0.557 0.5780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6116
## F-statistic: 157.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.611647068343424
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0500 -0.7838 0.0126 0.9269 3.8067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99422 0.08853 112.885 < 2e-16 ***
## category_code_LT01_2_count 0.86919 0.07123 12.203 < 2e-16 ***
## category_code_LT01_5_count 0.96791 0.06238 15.516 < 2e-16 ***
## category_code_LT01_7_count 0.61969 0.15348 4.037 6.27e-05 ***
## category_code_LT01_9_count 0.38691 0.22986 1.683 0.093 .
## category_code_LT01_14_count 0.20524 0.33115 0.620 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6156, Adjusted R-squared: 0.6116
## F-statistic: 157.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.611787490564105
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0507 -0.7815 0.0002 0.9275 3.8090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99194 0.08845 112.971 < 2e-16 ***
## category_code_LT01_2_count 0.86629 0.07154 12.109 < 2e-16 ***
## category_code_LT01_5_count 0.97216 0.06197 15.687 < 2e-16 ***
## category_code_LT01_7_count 0.62925 0.15285 4.117 4.51e-05 ***
## category_code_LT01_9_count 0.39867 0.22938 1.738 0.0828 .
## category_code_LT01_15_count 0.56785 0.75732 0.750 0.4537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 492 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.6118
## F-statistic: 157.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.611360123221235
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0509 -0.7834 0.0036 0.9535 3.8088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99219 0.08850 112.900 < 2e-16 ***
## category_code_LT01_2_count 0.87425 0.07106 12.303 < 2e-16 ***
## category_code_LT01_5_count 0.97211 0.06201 15.676 < 2e-16 ***
## category_code_LT01_7_count 0.62871 0.15298 4.110 4.64e-05 ***
## category_code_LT01_9_count 0.39497 0.22958 1.720 0.086 .
## category_code_LT01_16_count 0.17026 1.18705 0.143 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6114
## F-statistic: 157.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.618684858386227
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0313 -0.7677 0.0215 0.9316 3.8228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97811 0.09101 109.641 < 2e-16 ***
## category_code_LT01_2_count 0.71239 0.08687 8.200 2.1e-15 ***
## category_code_LT01_5_count 0.96411 0.06140 15.703 < 2e-16 ***
## category_code_LT01_7_count 0.48944 0.15787 3.100 0.002044 **
## category_code_LT01_10_count 0.11142 0.11303 0.986 0.324757
## category_code_LT01_11_count 0.39350 0.11695 3.365 0.000826 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6187
## F-statistic: 162.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.610212648291937
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0313 -0.7783 0.0064 0.9276 3.8309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97009 0.09198 108.393 < 2e-16 ***
## category_code_LT01_2_count 0.87784 0.07232 12.139 < 2e-16 ***
## category_code_LT01_5_count 0.97570 0.06226 15.672 < 2e-16 ***
## category_code_LT01_7_count 0.64116 0.15285 4.195 3.24e-05 ***
## category_code_LT01_10_count 0.12011 0.11433 1.051 0.294
## category_code_LT01_12_count 0.12907 0.20913 0.617 0.537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.610042823278471
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0336 -0.7915 0.0142 0.9297 3.8309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97005 0.09200 108.369 < 2e-16 ***
## category_code_LT01_2_count 0.88638 0.07030 12.608 < 2e-16 ***
## category_code_LT01_5_count 0.97908 0.06194 15.808 < 2e-16 ***
## category_code_LT01_7_count 0.63432 0.15422 4.113 4.58e-05 ***
## category_code_LT01_10_count 0.12199 0.11428 1.067 0.286
## category_code_LT01_13_count 0.10132 0.24833 0.408 0.683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.61
## F-statistic: 156.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.610117639365146
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0361 -0.7803 0.0132 0.9228 3.8264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97458 0.09241 107.933 < 2e-16 ***
## category_code_LT01_2_count 0.88464 0.07052 12.545 < 2e-16 ***
## category_code_LT01_5_count 0.97613 0.06233 15.662 < 2e-16 ***
## category_code_LT01_7_count 0.63647 0.15333 4.151 3.9e-05 ***
## category_code_LT01_10_count 0.11040 0.11688 0.945 0.345
## category_code_LT01_14_count 0.17305 0.33878 0.511 0.610
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6101
## F-statistic: 156.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.610227554514845
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0354 -0.7695 0.0015 0.9288 3.8296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97137 0.09200 108.382 < 2e-16 ***
## category_code_LT01_2_count 0.88214 0.07083 12.455 < 2e-16 ***
## category_code_LT01_5_count 0.97987 0.06189 15.832 < 2e-16 ***
## category_code_LT01_7_count 0.64449 0.15285 4.216 2.95e-05 ***
## category_code_LT01_10_count 0.11688 0.11464 1.020 0.308
## category_code_LT01_15_count 0.48143 0.76148 0.632 0.528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.609929829167977
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0343 -0.7894 0.0042 0.9283 3.8306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97040 0.09204 108.329 < 2e-16 ***
## category_code_LT01_2_count 0.88780 0.07053 12.587 < 2e-16 ***
## category_code_LT01_5_count 0.97966 0.06193 15.819 < 2e-16 ***
## category_code_LT01_7_count 0.64326 0.15293 4.206 3.09e-05 ***
## category_code_LT01_10_count 0.12235 0.11435 1.070 0.285
## category_code_LT01_16_count 0.18387 1.18954 0.155 0.877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6099
## F-statistic: 156.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.617966580492867
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0573 -0.7618 0.0159 0.9198 3.7983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00262 0.08773 114.022 < 2e-16 ***
## category_code_LT01_2_count 0.72358 0.08651 8.364 6.27e-16 ***
## category_code_LT01_5_count 0.96634 0.06171 15.661 < 2e-16 ***
## category_code_LT01_7_count 0.49947 0.15782 3.165 0.001648 **
## category_code_LT01_11_count 0.40360 0.12108 3.333 0.000922 ***
## category_code_LT01_12_count -0.04530 0.21406 -0.212 0.832496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.618002711774331
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0556 -0.7848 0.0176 0.9169 3.7988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00211 0.08771 114.034 < 2e-16 ***
## category_code_LT01_2_count 0.72100 0.08649 8.337 7.71e-16 ***
## category_code_LT01_5_count 0.96464 0.06147 15.694 < 2e-16 ***
## category_code_LT01_7_count 0.49575 0.15865 3.125 0.001885 **
## category_code_LT01_11_count 0.39562 0.11709 3.379 0.000786 ***
## category_code_LT01_13_count 0.07431 0.24592 0.302 0.762633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.618243632281981
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0551 -0.7698 0.0127 0.9137 3.7965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00441 0.08774 114.018 < 2e-16 ***
## category_code_LT01_2_count 0.71648 0.08684 8.251 1.45e-15 ***
## category_code_LT01_5_count 0.96062 0.06184 15.535 < 2e-16 ***
## category_code_LT01_7_count 0.49302 0.15805 3.119 0.001918 **
## category_code_LT01_11_count 0.39481 0.11702 3.374 0.000799 ***
## category_code_LT01_14_count 0.20781 0.32781 0.634 0.526419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6182
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.6180979105015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0562 -0.7628 -0.0024 0.9214 3.7987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00223 0.08770 114.052 < 2e-16 ***
## category_code_LT01_2_count 0.71888 0.08669 8.293 1.07e-15 ***
## category_code_LT01_5_count 0.96530 0.06144 15.712 < 2e-16 ***
## category_code_LT01_7_count 0.50388 0.15761 3.197 0.00148 **
## category_code_LT01_11_count 0.39274 0.11735 3.347 0.00088 ***
## category_code_LT01_15_count 0.34851 0.75339 0.463 0.64387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6181
## F-statistic: 161.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.617976382335287
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0563 -0.7629 0.0169 0.9216 3.7984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00260 0.08772 114.028 < 2e-16 ***
## category_code_LT01_2_count 0.72014 0.08690 8.286 1.12e-15 ***
## category_code_LT01_5_count 0.96485 0.06146 15.700 < 2e-16 ***
## category_code_LT01_7_count 0.50206 0.15757 3.186 0.00153 **
## category_code_LT01_11_count 0.39737 0.11701 3.396 0.00074 ***
## category_code_LT01_16_count 0.28187 1.17645 0.240 0.81075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.609473082049289
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0572 -0.7854 -0.0163 0.9190 3.8051
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99585 0.08868 112.724 < 2e-16 ***
## category_code_LT01_2_count 0.88693 0.07181 12.351 < 2e-16 ***
## category_code_LT01_5_count 0.97598 0.06233 15.658 < 2e-16 ***
## category_code_LT01_7_count 0.64690 0.15378 4.207 3.08e-05 ***
## category_code_LT01_12_count 0.13561 0.20924 0.648 0.517
## category_code_LT01_13_count 0.10247 0.24856 0.412 0.680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6095
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.609701147101862
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0571 -0.7880 -0.0065 0.9073 3.8025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99844 0.08872 112.702 < 2e-16 ***
## category_code_LT01_2_count 0.88305 0.07215 12.240 < 2e-16 ***
## category_code_LT01_5_count 0.97205 0.06267 15.512 < 2e-16 ***
## category_code_LT01_7_count 0.64557 0.15304 4.218 2.93e-05 ***
## category_code_LT01_12_count 0.12692 0.20975 0.605 0.545
## category_code_LT01_14_count 0.22480 0.33235 0.676 0.499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6097
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609761464415033
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0578 -0.7843 -0.0205 0.9115 3.8049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99604 0.08864 112.770 < 2e-16 ***
## category_code_LT01_2_count 0.88060 0.07254 12.139 < 2e-16 ***
## category_code_LT01_5_count 0.97657 0.06229 15.679 < 2e-16 ***
## category_code_LT01_7_count 0.65665 0.15234 4.310 1.97e-05 ***
## category_code_LT01_12_count 0.14037 0.20910 0.671 0.502
## category_code_LT01_15_count 0.55467 0.75928 0.731 0.465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6098
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.609372408989632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0580 -0.7859 -0.0192 0.9180 3.8046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99633 0.08869 112.708 < 2e-16 ***
## category_code_LT01_2_count 0.88763 0.07216 12.301 < 2e-16 ***
## category_code_LT01_5_count 0.97640 0.06233 15.665 < 2e-16 ***
## category_code_LT01_7_count 0.65610 0.15244 4.304 2.03e-05 ***
## category_code_LT01_12_count 0.13894 0.20922 0.664 0.507
## category_code_LT01_16_count 0.24702 1.18975 0.208 0.836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6133, Adjusted R-squared: 0.6094
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.609559642416961
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0596 -0.7885 -0.0026 0.9170 3.8020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99895 0.08872 112.696 < 2e-16 ***
## category_code_LT01_2_count 0.89086 0.07024 12.683 < 2e-16 ***
## category_code_LT01_5_count 0.97497 0.06240 15.625 < 2e-16 ***
## category_code_LT01_7_count 0.63767 0.15446 4.128 4.29e-05 ***
## category_code_LT01_13_count 0.10764 0.24843 0.433 0.665
## category_code_LT01_14_count 0.24107 0.33137 0.727 0.467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6096
## F-statistic: 156.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.609580499949421
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0607 -0.7694 -0.0164 0.9261 3.8045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99642 0.08866 112.753 < 2e-16 ***
## category_code_LT01_2_count 0.88970 0.07045 12.628 < 2e-16 ***
## category_code_LT01_5_count 0.98021 0.06196 15.820 < 2e-16 ***
## category_code_LT01_7_count 0.64889 0.15375 4.221 2.9e-05 ***
## category_code_LT01_13_count 0.11733 0.24880 0.472 0.637
## category_code_LT01_15_count 0.56686 0.76053 0.745 0.456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6096
## F-statistic: 156.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609175480093112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0609 -0.7696 -0.0130 0.9370 3.8042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99673 0.08871 112.689 < 2e-16 ***
## category_code_LT01_2_count 0.89697 0.07003 12.809 < 2e-16 ***
## category_code_LT01_5_count 0.98006 0.06201 15.806 < 2e-16 ***
## category_code_LT01_7_count 0.64898 0.15384 4.218 2.93e-05 ***
## category_code_LT01_13_count 0.10924 0.24875 0.439 0.661
## category_code_LT01_16_count 0.25289 1.19079 0.212 0.832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6092
## F-statistic: 155.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609805264459369
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0604 -0.7887 -0.0120 0.9158 3.8018
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99912 0.08869 112.736 < 2e-16 ***
## category_code_LT01_2_count 0.88559 0.07085 12.499 < 2e-16 ***
## category_code_LT01_5_count 0.97587 0.06235 15.651 < 2e-16 ***
## category_code_LT01_7_count 0.64813 0.15302 4.236 2.72e-05 ***
## category_code_LT01_14_count 0.23568 0.33134 0.711 0.477
## category_code_LT01_15_count 0.53561 0.75933 0.705 0.481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6098
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609455257719571
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0605 -0.7891 -0.0110 0.9159 3.8014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99954 0.08875 112.677 < 2e-16 ***
## category_code_LT01_2_count 0.89159 0.07059 12.631 < 2e-16 ***
## category_code_LT01_5_count 0.97541 0.06240 15.632 < 2e-16 ***
## category_code_LT01_7_count 0.64728 0.15309 4.228 2.81e-05 ***
## category_code_LT01_14_count 0.24517 0.33196 0.739 0.461
## category_code_LT01_16_count 0.28236 1.19136 0.237 0.813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6095
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609443423174882
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0617 -0.7660 -0.0237 0.9173 3.8040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99697 0.08868 112.732 < 2e-16 ***
## category_code_LT01_2_count 0.89119 0.07071 12.603 < 2e-16 ***
## category_code_LT01_5_count 0.98084 0.06196 15.831 < 2e-16 ***
## category_code_LT01_7_count 0.65941 0.15241 4.326 1.84e-05 ***
## category_code_LT01_15_count 0.55362 0.76007 0.728 0.467
## category_code_LT01_16_count 0.26519 1.19030 0.223 0.824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6094
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.612813403944354
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0603 -0.7909 0.0229 0.9003 4.3310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00133 0.08839 113.151 < 2e-16 ***
## category_code_LT01_2_count 0.73631 0.08716 8.448 3.37e-16 ***
## category_code_LT01_5_count 0.97251 0.06268 15.516 < 2e-16 ***
## category_code_LT01_8_count -0.07814 0.27749 -0.282 0.778
## category_code_LT01_9_count 0.42416 0.22853 1.856 0.064 .
## category_code_LT01_11_count 0.48866 0.11303 4.323 1.86e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.6128
## F-statistic: 158.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.611281991924444
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0407 -0.7644 0.0121 0.9043 4.3315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97631 0.09191 108.542 < 2e-16 ***
## category_code_LT01_2_count 0.74812 0.08697 8.602 < 2e-16 ***
## category_code_LT01_5_count 0.98027 0.06262 15.655 < 2e-16 ***
## category_code_LT01_8_count -0.06755 0.27793 -0.243 0.808
## category_code_LT01_10_count 0.13909 0.11383 1.222 0.222
## category_code_LT01_11_count 0.49779 0.11307 4.403 1.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6113
## F-statistic: 157.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.610218721773077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0737 -0.7740 0.0029 0.8902 4.2687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00703 0.08865 112.883 < 2e-16 ***
## category_code_LT01_2_count 0.76353 0.08647 8.830 < 2e-16 ***
## category_code_LT01_5_count 0.98359 0.06289 15.640 < 2e-16 ***
## category_code_LT01_8_count -0.05400 0.27848 -0.194 0.846
## category_code_LT01_11_count 0.51674 0.11682 4.423 1.2e-05 ***
## category_code_LT01_12_count -0.08281 0.21606 -0.383 0.702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.610445873038037
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0700 -0.7785 0.0111 0.8937 4.2823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00591 0.08862 112.908 < 2e-16 ***
## category_code_LT01_2_count 0.75755 0.08658 8.750 < 2e-16 ***
## category_code_LT01_5_count 0.97985 0.06273 15.619 < 2e-16 ***
## category_code_LT01_8_count -0.04875 0.27854 -0.175 0.861
## category_code_LT01_11_count 0.49975 0.11335 4.409 1.28e-05 ***
## category_code_LT01_13_count 0.16267 0.24697 0.659 0.510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6104
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.610734089923604
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0702 -0.7652 0.0030 0.8916 4.2894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00948 0.08864 112.919 < 2e-16 ***
## category_code_LT01_2_count 0.75240 0.08694 8.654 < 2e-16 ***
## category_code_LT01_5_count 0.97508 0.06308 15.457 < 2e-16 ***
## category_code_LT01_8_count -0.06335 0.27808 -0.228 0.820
## category_code_LT01_11_count 0.49973 0.11316 4.416 1.24e-05 ***
## category_code_LT01_14_count 0.29480 0.32991 0.894 0.372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.6107
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.610201490301193
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0720 -0.7812 -0.0008 0.8908 4.2782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00651 0.08864 112.886 < 2e-16 ***
## category_code_LT01_2_count 0.75920 0.08668 8.759 < 2e-16 ***
## category_code_LT01_5_count 0.98190 0.06270 15.661 < 2e-16 ***
## category_code_LT01_8_count -0.06012 0.27825 -0.216 0.829
## category_code_LT01_11_count 0.50256 0.11335 4.434 1.14e-05 ***
## category_code_LT01_15_count 0.26911 0.76078 0.354 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.610131930223064
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0720 -0.7730 0.0012 0.8958 4.2763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00681 0.08866 112.866 < 2e-16 ***
## category_code_LT01_2_count 0.75997 0.08690 8.745 < 2e-16 ***
## category_code_LT01_5_count 0.98154 0.06270 15.654 < 2e-16 ***
## category_code_LT01_8_count -0.06137 0.27860 -0.220 0.826
## category_code_LT01_11_count 0.50584 0.11308 4.473 9.57e-06 ***
## category_code_LT01_16_count 0.22984 1.18978 0.193 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6101
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.613547135717567
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0323 -0.7702 0.0257 0.9183 4.3775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97580 0.09161 108.888 < 2e-16 ***
## category_code_LT01_2_count 0.72625 0.08760 8.290 1.09e-15 ***
## category_code_LT01_5_count 0.96899 0.06186 15.663 < 2e-16 ***
## category_code_LT01_9_count 0.39421 0.22978 1.716 0.0869 .
## category_code_LT01_10_count 0.11504 0.11427 1.007 0.3145
## category_code_LT01_11_count 0.48384 0.11304 4.280 2.24e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 492 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6135
## F-statistic: 158.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.600089240446329
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0360 -0.8048 -0.0094 1.0031 3.9021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96428 0.09316 106.964 <2e-16 ***
## category_code_LT01_2_count 0.96038 0.06813 14.096 <2e-16 ***
## category_code_LT01_5_count 0.99084 0.06272 15.797 <2e-16 ***
## category_code_LT01_9_count 0.48146 0.23353 2.062 0.0398 *
## category_code_LT01_10_count 0.13240 0.11621 1.139 0.2552
## category_code_LT01_13_count 0.26749 0.24971 1.071 0.2846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 492 degrees of freedom
## Multiple R-squared: 0.6041, Adjusted R-squared: 0.6001
## F-statistic: 150.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.612862593420966
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0596 -0.7906 0.0148 0.9017 4.3284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00107 0.08835 113.199 < 2e-16 ***
## category_code_LT01_2_count 0.73771 0.08727 8.453 3.24e-16 ***
## category_code_LT01_5_count 0.97184 0.06215 15.636 < 2e-16 ***
## category_code_LT01_9_count 0.42094 0.22836 1.843 0.0659 .
## category_code_LT01_11_count 0.50011 0.11677 4.283 2.22e-05 ***
## category_code_LT01_12_count -0.08102 0.21513 -0.377 0.7066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6129
## F-statistic: 158.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.61323779773905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0555 -0.7893 0.0229 0.9148 4.3451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99976 0.08830 113.246 < 2e-16 ***
## category_code_LT01_2_count 0.73028 0.08740 8.355 6.72e-16 ***
## category_code_LT01_5_count 0.96777 0.06194 15.625 < 2e-16 ***
## category_code_LT01_9_count 0.43282 0.22867 1.893 0.059 .
## category_code_LT01_11_count 0.48178 0.11334 4.251 2.55e-05 ***
## category_code_LT01_13_count 0.19373 0.24619 0.787 0.432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6132
## F-statistic: 158.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.613212177334364
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0564 -0.7927 0.0253 0.9056 4.3452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00317 0.08837 113.203 < 2e-16 ***
## category_code_LT01_2_count 0.72861 0.08764 8.314 9.11e-16 ***
## category_code_LT01_5_count 0.96427 0.06230 15.477 < 2e-16 ***
## category_code_LT01_9_count 0.40957 0.22880 1.790 0.0741 .
## category_code_LT01_11_count 0.48465 0.11311 4.285 2.20e-05 ***
## category_code_LT01_14_count 0.25244 0.32960 0.766 0.4441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6132
## F-statistic: 158.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.61287312837482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0576 -0.7900 0.0057 0.9023 4.3389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00045 0.08834 113.208 < 2e-16 ***
## category_code_LT01_2_count 0.73291 0.08749 8.377 5.70e-16 ***
## category_code_LT01_5_count 0.96992 0.06191 15.666 < 2e-16 ***
## category_code_LT01_9_count 0.42378 0.22841 1.855 0.0641 .
## category_code_LT01_11_count 0.48577 0.11332 4.287 2.18e-05 ***
## category_code_LT01_15_count 0.29874 0.75828 0.394 0.6938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6129
## F-statistic: 158.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.612764142685258
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0577 -0.7903 0.0188 0.9068 4.3348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00070 0.08836 113.183 < 2e-16 ***
## category_code_LT01_2_count 0.73477 0.08765 8.383 5.46e-16 ***
## category_code_LT01_5_count 0.96964 0.06193 15.657 < 2e-16 ***
## category_code_LT01_9_count 0.42087 0.22848 1.842 0.0661 .
## category_code_LT01_11_count 0.48936 0.11306 4.328 1.82e-05 ***
## category_code_LT01_16_count 0.15309 1.18478 0.129 0.8972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.6128
## F-statistic: 158.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.611380852524642
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0403 -0.7656 0.0183 0.8863 4.3288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97604 0.09187 108.584 < 2e-16 ***
## category_code_LT01_2_count 0.74956 0.08704 8.611 < 2e-16 ***
## category_code_LT01_5_count 0.98018 0.06205 15.795 < 2e-16 ***
## category_code_LT01_10_count 0.13982 0.11382 1.228 0.220
## category_code_LT01_11_count 0.51061 0.11675 4.374 1.49e-05 ***
## category_code_LT01_12_count -0.09255 0.21563 -0.429 0.668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6114
## F-statistic: 157.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.61155301854773
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0374 -0.7653 0.0288 0.8994 4.3408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97571 0.09185 108.607 < 2e-16 ***
## category_code_LT01_2_count 0.74393 0.08713 8.538 < 2e-16 ***
## category_code_LT01_5_count 0.97648 0.06184 15.789 < 2e-16 ***
## category_code_LT01_10_count 0.13623 0.11380 1.197 0.232
## category_code_LT01_11_count 0.49269 0.11334 4.347 1.68e-05 ***
## category_code_LT01_13_count 0.15628 0.24636 0.634 0.526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6116
## F-statistic: 157.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.611561918062364
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0408 -0.7710 0.0303 0.9016 4.3383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98140 0.09227 108.177 < 2e-16 ***
## category_code_LT01_2_count 0.74241 0.08732 8.502 2.24e-16 ***
## category_code_LT01_5_count 0.97309 0.06225 15.632 < 2e-16 ***
## category_code_LT01_10_count 0.12221 0.11649 1.049 0.295
## category_code_LT01_11_count 0.49478 0.11314 4.373 1.50e-05 ***
## category_code_LT01_14_count 0.21704 0.33745 0.643 0.520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6116
## F-statistic: 157.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.611286741191473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0390 -0.7658 0.0168 0.8955 4.3358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97622 0.09190 108.554 < 2e-16 ***
## category_code_LT01_2_count 0.74609 0.08720 8.556 < 2e-16 ***
## category_code_LT01_5_count 0.97801 0.06183 15.818 < 2e-16 ***
## category_code_LT01_10_count 0.13605 0.11415 1.192 0.234
## category_code_LT01_11_count 0.49613 0.11332 4.378 1.46e-05 ***
## category_code_LT01_15_count 0.19440 0.76206 0.255 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6113
## F-statistic: 157.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.611250383606305
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0386 -0.7656 0.0182 0.8947 4.3351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97605 0.09191 108.540 < 2e-16 ***
## category_code_LT01_2_count 0.74649 0.08745 8.536 < 2e-16 ***
## category_code_LT01_5_count 0.97771 0.06184 15.810 < 2e-16 ***
## category_code_LT01_10_count 0.13779 0.11387 1.210 0.227
## category_code_LT01_11_count 0.49841 0.11309 4.407 1.29e-05 ***
## category_code_LT01_16_count 0.16391 1.18739 0.138 0.890
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 492 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6113
## F-statistic: 157.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.610548690591714
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0703 -0.7692 -0.0031 0.8977 4.2793
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00594 0.08857 112.974 < 2e-16 ***
## category_code_LT01_2_count 0.75896 0.08665 8.759 < 2e-16 ***
## category_code_LT01_5_count 0.98026 0.06215 15.773 < 2e-16 ***
## category_code_LT01_11_count 0.51157 0.11705 4.371 1.51e-05 ***
## category_code_LT01_12_count -0.08645 0.21578 -0.401 0.689
## category_code_LT01_13_count 0.16624 0.24659 0.674 0.501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6105
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.610856379709229
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0703 -0.7774 -0.0015 0.8885 4.2865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00954 0.08859 112.984 < 2e-16 ***
## category_code_LT01_2_count 0.75377 0.08700 8.664 < 2e-16 ***
## category_code_LT01_5_count 0.97509 0.06250 15.601 < 2e-16 ***
## category_code_LT01_11_count 0.51317 0.11679 4.394 1.36e-05 ***
## category_code_LT01_12_count -0.09825 0.21618 -0.454 0.650
## category_code_LT01_14_count 0.30368 0.33058 0.919 0.359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6109
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.610279105713581
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0718 -0.7695 -0.0152 0.8909 4.2753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00641 0.08860 112.945 < 2e-16 ***
## category_code_LT01_2_count 0.76066 0.08679 8.765 < 2e-16 ***
## category_code_LT01_5_count 0.98181 0.06213 15.802 < 2e-16 ***
## category_code_LT01_11_count 0.51410 0.11715 4.388 1.4e-05 ***
## category_code_LT01_12_count -0.08214 0.21596 -0.380 0.704
## category_code_LT01_15_count 0.25678 0.76107 0.337 0.736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6103
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610213595166886
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0719 -0.7689 -0.0063 0.8909 4.2733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00668 0.08861 112.928 < 2e-16 ***
## category_code_LT01_2_count 0.76150 0.08701 8.752 < 2e-16 ***
## category_code_LT01_5_count 0.98148 0.06215 15.793 < 2e-16 ***
## category_code_LT01_11_count 0.51748 0.11681 4.430 1.16e-05 ***
## category_code_LT01_12_count -0.08406 0.21588 -0.389 0.697
## category_code_LT01_16_count 0.20966 1.18831 0.176 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611041965000081
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0665 -0.7791 0.0157 0.8933 4.3001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00825 0.08856 113.010 < 2e-16 ***
## category_code_LT01_2_count 0.74785 0.08712 8.584 < 2e-16 ***
## category_code_LT01_5_count 0.97140 0.06233 15.585 < 2e-16 ***
## category_code_LT01_11_count 0.49424 0.11344 4.357 1.61e-05 ***
## category_code_LT01_13_count 0.16371 0.24642 0.664 0.507
## category_code_LT01_14_count 0.29208 0.32973 0.886 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.615, Adjusted R-squared: 0.611
## F-statistic: 157.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.610543848557855
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0682 -0.7807 0.0015 0.8914 4.2901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00531 0.08856 112.980 < 2e-16 ***
## category_code_LT01_2_count 0.75409 0.08690 8.678 < 2e-16 ***
## category_code_LT01_5_count 0.97822 0.06191 15.800 < 2e-16 ***
## category_code_LT01_11_count 0.49643 0.11367 4.367 1.53e-05 ***
## category_code_LT01_13_count 0.17102 0.24705 0.692 0.489
## category_code_LT01_15_count 0.29936 0.76183 0.393 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6105
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.61045662542751
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0683 -0.7762 0.0056 0.8964 4.2877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00563 0.08858 112.961 < 2e-16 ***
## category_code_LT01_2_count 0.75509 0.08712 8.667 < 2e-16 ***
## category_code_LT01_5_count 0.97782 0.06193 15.789 < 2e-16 ***
## category_code_LT01_11_count 0.50021 0.11336 4.413 1.26e-05 ***
## category_code_LT01_13_count 0.16715 0.24681 0.677 0.499
## category_code_LT01_16_count 0.24995 1.18881 0.210 0.834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6105
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610782630980548
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0681 -0.7597 0.0037 0.8874 4.2960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00873 0.08859 112.983 < 2e-16 ***
## category_code_LT01_2_count 0.74960 0.08723 8.594 < 2e-16 ***
## category_code_LT01_5_count 0.97305 0.06231 15.615 < 2e-16 ***
## category_code_LT01_11_count 0.49729 0.11343 4.384 1.42e-05 ***
## category_code_LT01_14_count 0.29159 0.32988 0.884 0.377
## category_code_LT01_15_count 0.25586 0.76023 0.337 0.737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.6108
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610736584605665
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0681 -0.7595 0.0085 0.8892 4.2955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00913 0.08860 112.967 < 2e-16 ***
## category_code_LT01_2_count 0.74963 0.08754 8.563 < 2e-16 ***
## category_code_LT01_5_count 0.97247 0.06234 15.600 < 2e-16 ***
## category_code_LT01_11_count 0.50040 0.11316 4.422 1.2e-05 ***
## category_code_LT01_14_count 0.29789 0.33041 0.902 0.368
## category_code_LT01_16_count 0.27908 1.18938 0.235 0.815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.6107
## F-statistic: 157 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610194773495332
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0699 -0.7784 -0.0018 0.8903 4.2834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00611 0.08860 112.933 < 2e-16 ***
## category_code_LT01_2_count 0.75689 0.08724 8.676 < 2e-16 ***
## category_code_LT01_5_count 0.97952 0.06191 15.821 < 2e-16 ***
## category_code_LT01_11_count 0.50316 0.11336 4.439 1.12e-05 ***
## category_code_LT01_15_count 0.27219 0.76122 0.358 0.721
## category_code_LT01_16_count 0.23238 1.18900 0.195 0.845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.631630320481935
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9792 -0.7364 0.0115 0.9312 3.4791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97442 0.08624 115.662 < 2e-16 ***
## category_code_LT01_3_count 0.32838 0.11166 2.941 0.00343 **
## category_code_LT01_4_count 0.74294 0.09004 8.251 1.45e-15 ***
## category_code_LT01_5_count 0.89428 0.06153 14.534 < 2e-16 ***
## category_code_LT01_6_count 0.44774 0.14881 3.009 0.00276 **
## category_code_LT01_7_count 0.49249 0.15168 3.247 0.00125 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 492 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6316
## F-statistic: 171.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.62406687306559
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9927 -0.7514 0.0121 0.9037 3.4573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97633 0.08715 114.471 < 2e-16 ***
## category_code_LT01_3_count 0.34915 0.11266 3.099 0.00205 **
## category_code_LT01_4_count 0.84058 0.08572 9.806 < 2e-16 ***
## category_code_LT01_5_count 0.91111 0.06272 14.526 < 2e-16 ***
## category_code_LT01_6_count 0.45780 0.15042 3.043 0.00246 **
## category_code_LT01_8_count -0.17972 0.27355 -0.657 0.51149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6241
## F-statistic: 166 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.626063628257118
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9782 -0.7556 0.0284 0.9223 3.5021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97077 0.08692 114.717 < 2e-16 ***
## category_code_LT01_3_count 0.31759 0.11367 2.794 0.00541 **
## category_code_LT01_4_count 0.82662 0.08587 9.626 < 2e-16 ***
## category_code_LT01_5_count 0.89817 0.06203 14.480 < 2e-16 ***
## category_code_LT01_6_count 0.43960 0.15015 2.928 0.00357 **
## category_code_LT01_9_count 0.39465 0.22556 1.750 0.08081 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6261
## F-statistic: 167.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.623939298505299
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9752 -0.7465 -0.0150 0.9220 3.4122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96267 0.09024 110.400 < 2e-16 ***
## category_code_LT01_3_count 0.33790 0.11432 2.956 0.00327 **
## category_code_LT01_4_count 0.84050 0.08574 9.803 < 2e-16 ***
## category_code_LT01_5_count 0.90556 0.06208 14.587 < 2e-16 ***
## category_code_LT01_6_count 0.44232 0.15209 2.908 0.00380 **
## category_code_LT01_10_count 0.05901 0.11471 0.514 0.60721
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6239
## F-statistic: 165.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.630779471648376
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9922 -0.7432 0.0268 0.9123 3.4616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98212 0.08637 115.573 < 2e-16 ***
## category_code_LT01_3_count 0.26067 0.11520 2.263 0.02409 *
## category_code_LT01_4_count 0.70944 0.09515 7.456 4.05e-13 ***
## category_code_LT01_5_count 0.90193 0.06152 14.661 < 2e-16 ***
## category_code_LT01_6_count 0.37702 0.15108 2.495 0.01290 *
## category_code_LT01_11_count 0.34883 0.11387 3.063 0.00231 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 492 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.6308
## F-statistic: 170.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.623833827240932
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9856 -0.7522 0.0096 0.9193 3.4675
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97454 0.08715 114.456 < 2e-16 ***
## category_code_LT01_3_count 0.34503 0.11296 3.055 0.00238 **
## category_code_LT01_4_count 0.83698 0.08639 9.689 < 2e-16 ***
## category_code_LT01_5_count 0.90338 0.06229 14.503 < 2e-16 ***
## category_code_LT01_6_count 0.44762 0.15148 2.955 0.00328 **
## category_code_LT01_12_count 0.07330 0.20602 0.356 0.72216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6276, Adjusted R-squared: 0.6238
## F-statistic: 165.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.623967754200153
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7497 0.0106 0.9140 3.4666
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97447 0.08713 114.476 < 2e-16 ***
## category_code_LT01_3_count 0.34655 0.11268 3.075 0.00222 **
## category_code_LT01_4_count 0.83418 0.08655 9.638 < 2e-16 ***
## category_code_LT01_5_count 0.90426 0.06210 14.562 < 2e-16 ***
## category_code_LT01_6_count 0.45510 0.15035 3.027 0.00260 **
## category_code_LT01_13_count 0.13327 0.24257 0.549 0.58297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.624
## F-statistic: 165.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.624273682069829
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9853 -0.7420 -0.0024 0.9244 3.4696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97718 0.08714 114.493 < 2e-16 ***
## category_code_LT01_3_count 0.35068 0.11266 3.113 0.00196 **
## category_code_LT01_4_count 0.82308 0.08824 9.328 < 2e-16 ***
## category_code_LT01_5_count 0.89913 0.06246 14.394 < 2e-16 ***
## category_code_LT01_6_count 0.46462 0.15079 3.081 0.00218 **
## category_code_LT01_14_count 0.27469 0.32768 0.838 0.40229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 492 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6243
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.623750542947682
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9868 -0.7542 0.0052 0.9228 3.4657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97464 0.08716 114.442 < 2e-16 ***
## category_code_LT01_3_count 0.34978 0.11359 3.079 0.00219 **
## category_code_LT01_4_count 0.84135 0.08590 9.795 < 2e-16 ***
## category_code_LT01_5_count 0.90492 0.06212 14.568 < 2e-16 ***
## category_code_LT01_6_count 0.45494 0.15050 3.023 0.00263 **
## category_code_LT01_15_count -0.10012 0.75376 -0.133 0.89439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 492 degrees of freedom
## Multiple R-squared: 0.6275, Adjusted R-squared: 0.6238
## F-statistic: 165.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.624354197803983
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7460 0.0018 0.9178 3.4681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97494 0.08709 114.542 < 2e-16 ***
## category_code_LT01_3_count 0.33419 0.11362 2.941 0.00342 **
## category_code_LT01_4_count 0.83995 0.08569 9.802 < 2e-16 ***
## category_code_LT01_5_count 0.90362 0.06207 14.559 < 2e-16 ***
## category_code_LT01_6_count 0.46458 0.15071 3.083 0.00217 **
## category_code_LT01_16_count 1.05308 1.17131 0.899 0.36906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6244
## F-statistic: 166.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.62517133206472
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0160 -0.7632 0.0384 0.8551 3.4304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99356 0.08683 115.090 < 2e-16 ***
## category_code_LT01_3_count 0.36942 0.11187 3.302 0.00103 **
## category_code_LT01_4_count 0.82873 0.08615 9.620 < 2e-16 ***
## category_code_LT01_5_count 0.91986 0.06240 14.742 < 2e-16 ***
## category_code_LT01_7_count 0.50161 0.15306 3.277 0.00112 **
## category_code_LT01_8_count -0.17674 0.27309 -0.647 0.51782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.62676842693814
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0017 -0.7390 0.0459 0.8598 3.4497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98776 0.08665 115.270 < 2e-16 ***
## category_code_LT01_3_count 0.34023 0.11297 3.012 0.00273 **
## category_code_LT01_4_count 0.81841 0.08621 9.493 < 2e-16 ***
## category_code_LT01_5_count 0.90754 0.06172 14.705 < 2e-16 ***
## category_code_LT01_7_count 0.47348 0.15348 3.085 0.00215 **
## category_code_LT01_9_count 0.35951 0.22620 1.589 0.11263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.6268
## F-statistic: 167.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.625239349915722
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9931 -0.7544 0.0488 0.8671 3.3669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97468 0.09007 110.739 < 2e-16 ***
## category_code_LT01_3_count 0.35319 0.11375 3.105 0.00201 **
## category_code_LT01_4_count 0.82707 0.08617 9.598 < 2e-16 ***
## category_code_LT01_5_count 0.91394 0.06171 14.810 < 2e-16 ***
## category_code_LT01_7_count 0.48994 0.15345 3.193 0.00150 **
## category_code_LT01_10_count 0.08095 0.11355 0.713 0.47625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.630686919822671
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0119 -0.7818 0.0588 0.8768 3.4385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99605 0.08617 116.010 < 2e-16 ***
## category_code_LT01_3_count 0.28522 0.11493 2.482 0.01341 *
## category_code_LT01_4_count 0.71536 0.09469 7.555 2.06e-13 ***
## category_code_LT01_5_count 0.91028 0.06128 14.856 < 2e-16 ***
## category_code_LT01_7_count 0.38777 0.15697 2.470 0.01384 *
## category_code_LT01_11_count 0.32361 0.11607 2.788 0.00551 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 492 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6307
## F-statistic: 170.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.625200674546193
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0067 -0.7612 0.0509 0.8718 3.4438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99099 0.08680 115.104 < 2e-16 ***
## category_code_LT01_3_count 0.36157 0.11225 3.221 0.00136 **
## category_code_LT01_4_count 0.81965 0.08719 9.400 < 2e-16 ***
## category_code_LT01_5_count 0.91005 0.06198 14.683 < 2e-16 ***
## category_code_LT01_7_count 0.49715 0.15300 3.249 0.00124 **
## category_code_LT01_12_count 0.13807 0.20415 0.676 0.49917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.624865129085416
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0101 -0.7632 0.0494 0.8567 3.4388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99182 0.08683 115.074 < 2e-16 ***
## category_code_LT01_3_count 0.36777 0.11191 3.286 0.00109 **
## category_code_LT01_4_count 0.82774 0.08654 9.564 < 2e-16 ***
## category_code_LT01_5_count 0.91377 0.06175 14.797 < 2e-16 ***
## category_code_LT01_7_count 0.49616 0.15414 3.219 0.00137 **
## category_code_LT01_13_count 0.03172 0.24398 0.130 0.89663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6249
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.62494538807273
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0099 -0.7633 0.0475 0.8672 3.4399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99306 0.08689 115.014 < 2e-16 ***
## category_code_LT01_3_count 0.36969 0.11199 3.301 0.00103 **
## category_code_LT01_4_count 0.82307 0.08770 9.385 < 2e-16 ***
## category_code_LT01_5_count 0.91169 0.06206 14.690 < 2e-16 ***
## category_code_LT01_7_count 0.49464 0.15344 3.224 0.00135 **
## category_code_LT01_14_count 0.11435 0.32713 0.350 0.72683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6249
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.624858931349179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0104 -0.7636 0.0462 0.8614 3.4384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99193 0.08683 115.073 < 2e-16 ***
## category_code_LT01_3_count 0.36657 0.11290 3.247 0.00125 **
## category_code_LT01_4_count 0.82811 0.08647 9.577 < 2e-16 ***
## category_code_LT01_5_count 0.91405 0.06176 14.800 < 2e-16 ***
## category_code_LT01_7_count 0.49901 0.15313 3.259 0.00120 **
## category_code_LT01_15_count 0.07046 0.75246 0.094 0.92543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6249
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.625200054978838
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0101 -0.7629 0.0404 0.8657 3.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99232 0.08679 115.129 < 2e-16 ***
## category_code_LT01_3_count 0.35841 0.11274 3.179 0.00157 **
## category_code_LT01_4_count 0.82966 0.08615 9.630 < 2e-16 ***
## category_code_LT01_5_count 0.91310 0.06173 14.793 < 2e-16 ***
## category_code_LT01_7_count 0.49890 0.15298 3.261 0.00119 **
## category_code_LT01_16_count 0.78822 1.16653 0.676 0.49955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.619827451594698
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0133 -0.7781 0.0502 0.8840 3.4321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98903 0.08749 114.179 < 2e-16 ***
## category_code_LT01_3_count 0.35492 0.11394 3.115 0.00195 **
## category_code_LT01_4_count 0.91088 0.08162 11.160 < 2e-16 ***
## category_code_LT01_5_count 0.92243 0.06289 14.667 < 2e-16 ***
## category_code_LT01_8_count -0.16525 0.27503 -0.601 0.54823
## category_code_LT01_9_count 0.43542 0.22719 1.917 0.05588 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6198
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.617717416531526
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0001 -0.7600 0.0429 0.8660 3.3206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97042 0.09098 109.586 <2e-16 ***
## category_code_LT01_3_count 0.36897 0.11481 3.214 0.0014 **
## category_code_LT01_4_count 0.92480 0.08141 11.359 <2e-16 ***
## category_code_LT01_5_count 0.93023 0.06293 14.782 <2e-16 ***
## category_code_LT01_8_count -0.15419 0.27571 -0.559 0.5763
## category_code_LT01_10_count 0.11070 0.11435 0.968 0.3335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6177
## F-statistic: 161.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.626262690165262
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7703 0.0444 0.8745 3.4231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99858 0.08672 115.302 < 2e-16 ***
## category_code_LT01_3_count 0.28291 0.11565 2.446 0.014779 *
## category_code_LT01_4_count 0.76386 0.09330 8.187 2.31e-15 ***
## category_code_LT01_5_count 0.92192 0.06227 14.805 < 2e-16 ***
## category_code_LT01_8_count -0.12381 0.27266 -0.454 0.649962
## category_code_LT01_11_count 0.39482 0.11300 3.494 0.000519 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6263
## F-statistic: 167.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.617406191824437
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0199 -0.7747 0.0420 0.8707 3.4239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99299 0.08774 113.899 < 2e-16 ***
## category_code_LT01_3_count 0.38261 0.11326 3.378 0.000788 ***
## category_code_LT01_4_count 0.91921 0.08250 11.141 < 2e-16 ***
## category_code_LT01_5_count 0.92626 0.06320 14.657 < 2e-16 ***
## category_code_LT01_8_count -0.15611 0.27594 -0.566 0.571845
## category_code_LT01_12_count 0.15111 0.20637 0.732 0.464384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6174
## F-statistic: 161.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.617168184234785
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0226 -0.7832 0.0465 0.8574 3.4200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99361 0.08776 113.879 < 2e-16 ***
## category_code_LT01_3_count 0.38845 0.11292 3.440 0.000631 ***
## category_code_LT01_4_count 0.92387 0.08217 11.244 < 2e-16 ***
## category_code_LT01_5_count 0.92927 0.06301 14.748 < 2e-16 ***
## category_code_LT01_8_count -0.14129 0.27633 -0.511 0.609359
## category_code_LT01_13_count 0.11755 0.24515 0.480 0.631787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6172
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.617258417126852
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0230 -0.7793 0.0484 0.8549 3.4208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99592 0.08781 113.834 < 2e-16 ***
## category_code_LT01_3_count 0.39227 0.11297 3.472 0.000561 ***
## category_code_LT01_4_count 0.91849 0.08344 11.008 < 2e-16 ***
## category_code_LT01_5_count 0.92645 0.06331 14.634 < 2e-16 ***
## category_code_LT01_8_count -0.15151 0.27586 -0.549 0.583087
## category_code_LT01_14_count 0.19389 0.32963 0.588 0.556669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.616989317219554
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0236 -0.7834 0.0348 0.8594 3.4186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.993889 0.087777 113.855 < 2e-16 ***
## category_code_LT01_3_count 0.389732 0.113904 3.422 0.000674 ***
## category_code_LT01_4_count 0.929531 0.081576 11.395 < 2e-16 ***
## category_code_LT01_5_count 0.930279 0.063012 14.764 < 2e-16 ***
## category_code_LT01_8_count -0.148970 0.275933 -0.540 0.589526
## category_code_LT01_15_count -0.005863 0.759932 -0.008 0.993847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.61735340605865
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0238 -0.7840 0.0346 0.8583 3.4187
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99444 0.08774 113.914 < 2e-16 ***
## category_code_LT01_3_count 0.37991 0.11375 3.340 0.000902 ***
## category_code_LT01_4_count 0.93049 0.08132 11.442 < 2e-16 ***
## category_code_LT01_5_count 0.92977 0.06297 14.766 < 2e-16 ***
## category_code_LT01_8_count -0.15802 0.27611 -0.572 0.567370
## category_code_LT01_16_count 0.80743 1.18002 0.684 0.494139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6174
## F-statistic: 161.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.620003115715513
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9895 -0.7586 0.0647 0.8870 3.3692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96902 0.09068 109.931 < 2e-16 ***
## category_code_LT01_3_count 0.33880 0.11557 2.932 0.00353 **
## category_code_LT01_4_count 0.90757 0.08169 11.109 < 2e-16 ***
## category_code_LT01_5_count 0.91705 0.06220 14.744 < 2e-16 ***
## category_code_LT01_9_count 0.41303 0.22827 1.809 0.07099 .
## category_code_LT01_10_count 0.08793 0.11460 0.767 0.44333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.62
## F-statistic: 163.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.628288551716823
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0089 -0.7657 0.0583 0.8966 3.4414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99285 0.08648 115.545 < 2e-16 ***
## category_code_LT01_3_count 0.25415 0.11640 2.183 0.029481 *
## category_code_LT01_4_count 0.75163 0.09325 8.060 5.83e-15 ***
## category_code_LT01_5_count 0.91053 0.06154 14.795 < 2e-16 ***
## category_code_LT01_9_count 0.38242 0.22500 1.700 0.089831 .
## category_code_LT01_11_count 0.38395 0.11289 3.401 0.000725 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 492 degrees of freedom
## Multiple R-squared: 0.632, Adjusted R-squared: 0.6283
## F-statistic: 169 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.619921060843282
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0042 -0.7762 0.0499 0.8922 3.4450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98660 0.08745 114.201 < 2e-16 ***
## category_code_LT01_3_count 0.34725 0.11430 3.038 0.00251 **
## category_code_LT01_4_count 0.90087 0.08279 10.882 < 2e-16 ***
## category_code_LT01_5_count 0.91289 0.06247 14.613 < 2e-16 ***
## category_code_LT01_9_count 0.42949 0.22707 1.891 0.05915 .
## category_code_LT01_12_count 0.14277 0.20558 0.694 0.48773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6199
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.619853914060397
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0067 -0.7744 0.0625 0.8845 3.4414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98711 0.08745 114.207 < 2e-16 ***
## category_code_LT01_3_count 0.35165 0.11398 3.085 0.00215 **
## category_code_LT01_4_count 0.90285 0.08253 10.940 < 2e-16 ***
## category_code_LT01_5_count 0.91573 0.06224 14.713 < 2e-16 ***
## category_code_LT01_9_count 0.43992 0.22750 1.934 0.05372 .
## category_code_LT01_13_count 0.15361 0.24433 0.629 0.52983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6199
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619696789507269
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7671 0.0524 0.8813 3.4412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98907 0.08754 114.112 < 2e-16 ***
## category_code_LT01_3_count 0.35636 0.11410 3.123 0.00189 **
## category_code_LT01_4_count 0.90266 0.08359 10.799 < 2e-16 ***
## category_code_LT01_5_count 0.91411 0.06255 14.615 < 2e-16 ***
## category_code_LT01_9_count 0.42360 0.22779 1.860 0.06354 .
## category_code_LT01_14_count 0.14432 0.32950 0.438 0.66158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6197
## F-statistic: 163 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.619551060130201
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0080 -0.7686 0.0496 0.8842 3.4396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98752 0.08748 114.166 < 2e-16 ***
## category_code_LT01_3_count 0.35289 0.11504 3.068 0.00228 **
## category_code_LT01_4_count 0.91015 0.08191 11.112 < 2e-16 ***
## category_code_LT01_5_count 0.91697 0.06225 14.730 < 2e-16 ***
## category_code_LT01_9_count 0.43169 0.22732 1.899 0.05815 .
## category_code_LT01_15_count 0.04358 0.75787 0.058 0.95417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6196
## F-statistic: 162.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.619833800918763
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7681 0.0467 0.8850 3.4400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98794 0.08745 114.212 < 2e-16 ***
## category_code_LT01_3_count 0.34544 0.11475 3.010 0.00274 **
## category_code_LT01_4_count 0.91156 0.08164 11.166 < 2e-16 ***
## category_code_LT01_5_count 0.91623 0.06222 14.725 < 2e-16 ***
## category_code_LT01_9_count 0.42740 0.22717 1.881 0.06051 .
## category_code_LT01_16_count 0.71415 1.17529 0.608 0.54371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6198
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.626706931681075
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9969 -0.7579 0.0515 0.8791 3.3401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97601 0.08990 110.965 < 2e-16 ***
## category_code_LT01_3_count 0.26326 0.11733 2.244 0.02529 *
## category_code_LT01_4_count 0.75956 0.09329 8.142 3.22e-15 ***
## category_code_LT01_5_count 0.91751 0.06153 14.911 < 2e-16 ***
## category_code_LT01_10_count 0.10056 0.11300 0.890 0.37394
## category_code_LT01_11_count 0.39391 0.11292 3.488 0.00053 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.6267
## F-statistic: 167.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.617832025433061
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9922 -0.7585 0.0527 0.8820 3.3366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96892 0.09094 109.616 < 2e-16 ***
## category_code_LT01_3_count 0.36181 0.11512 3.143 0.00177 **
## category_code_LT01_4_count 0.91494 0.08257 11.081 < 2e-16 ***
## category_code_LT01_5_count 0.92104 0.06249 14.739 < 2e-16 ***
## category_code_LT01_10_count 0.10661 0.11439 0.932 0.35180
## category_code_LT01_12_count 0.13996 0.20627 0.679 0.49777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6178
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.617656087217571
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7589 0.0532 0.8682 3.3313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96933 0.09096 109.596 < 2e-16 ***
## category_code_LT01_3_count 0.36701 0.11481 3.197 0.00148 **
## category_code_LT01_4_count 0.91879 0.08222 11.174 < 2e-16 ***
## category_code_LT01_5_count 0.92423 0.06225 14.846 < 2e-16 ***
## category_code_LT01_10_count 0.10788 0.11438 0.943 0.34605
## category_code_LT01_13_count 0.11830 0.24467 0.483 0.62896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6177
## F-statistic: 161.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.617582663122339
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9969 -0.7617 0.0565 0.8695 3.3391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97261 0.09143 109.074 < 2e-16 ***
## category_code_LT01_3_count 0.37140 0.11521 3.224 0.00135 **
## category_code_LT01_4_count 0.91761 0.08340 11.002 < 2e-16 ***
## category_code_LT01_5_count 0.92245 0.06261 14.733 < 2e-16 ***
## category_code_LT01_10_count 0.09954 0.11739 0.848 0.39686
## category_code_LT01_14_count 0.12621 0.33822 0.373 0.70918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6176
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.617478319852585
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9950 -0.7586 0.0478 0.8706 3.3286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96906 0.09101 109.538 < 2e-16 ***
## category_code_LT01_3_count 0.36880 0.11564 3.189 0.00152 **
## category_code_LT01_4_count 0.92476 0.08164 11.327 < 2e-16 ***
## category_code_LT01_5_count 0.92488 0.06227 14.853 < 2e-16 ***
## category_code_LT01_10_count 0.10995 0.11458 0.960 0.33774
## category_code_LT01_15_count -0.05390 0.76083 -0.071 0.94355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.617784204755381
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9956 -0.7596 0.0447 0.8786 3.3311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97006 0.09096 109.610 <2e-16 ***
## category_code_LT01_3_count 0.35919 0.11559 3.107 0.002 **
## category_code_LT01_4_count 0.92534 0.08142 11.365 <2e-16 ***
## category_code_LT01_5_count 0.92423 0.06224 14.850 <2e-16 ***
## category_code_LT01_10_count 0.10743 0.11436 0.939 0.348
## category_code_LT01_16_count 0.74418 1.17847 0.631 0.528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6178
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626136691026662
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0195 -0.7617 0.0476 0.8757 3.4274
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99776 0.08671 115.301 < 2e-16 ***
## category_code_LT01_3_count 0.28178 0.11563 2.437 0.015169 *
## category_code_LT01_4_count 0.76320 0.09330 8.180 2.44e-15 ***
## category_code_LT01_5_count 0.91877 0.06181 14.864 < 2e-16 ***
## category_code_LT01_11_count 0.40231 0.11703 3.438 0.000637 ***
## category_code_LT01_12_count -0.04241 0.21119 -0.201 0.840943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6261
## F-statistic: 167.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62618339566163
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7584 0.0503 0.8773 3.4296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99725 0.08669 115.323 < 2e-16 ***
## category_code_LT01_3_count 0.28127 0.11561 2.433 0.01534 *
## category_code_LT01_4_count 0.76007 0.09370 8.112 4.01e-15 ***
## category_code_LT01_5_count 0.91723 0.06159 14.892 < 2e-16 ***
## category_code_LT01_11_count 0.39415 0.11315 3.483 0.00054 ***
## category_code_LT01_13_count 0.07728 0.24222 0.319 0.74982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6262
## F-statistic: 167.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626259883686552
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.7554 0.0520 0.8734 3.4307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99890 0.08674 115.273 < 2e-16 ***
## category_code_LT01_3_count 0.28398 0.11574 2.454 0.014489 *
## category_code_LT01_4_count 0.75539 0.09475 7.973 1.09e-14 ***
## category_code_LT01_5_count 0.91475 0.06191 14.775 < 2e-16 ***
## category_code_LT01_11_count 0.39419 0.11305 3.487 0.000532 ***
## category_code_LT01_14_count 0.14668 0.32594 0.450 0.652905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6263
## F-statistic: 167.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.62611869868685
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0182 -0.7589 0.0445 0.8770 3.4292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9973 0.0867 115.314 < 2e-16 ***
## category_code_LT01_3_count 0.2832 0.1164 2.432 0.015358 *
## category_code_LT01_4_count 0.7635 0.0934 8.174 2.54e-15 ***
## category_code_LT01_5_count 0.9175 0.0616 14.894 < 2e-16 ***
## category_code_LT01_11_count 0.3967 0.1130 3.509 0.000491 ***
## category_code_LT01_15_count -0.0969 0.7512 -0.129 0.897414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6261
## F-statistic: 167.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.626413242492989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0183 -0.7576 0.0464 0.8810 3.4294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99782 0.08666 115.364 < 2e-16 ***
## category_code_LT01_3_count 0.27264 0.11641 2.342 0.019572 *
## category_code_LT01_4_count 0.76404 0.09327 8.192 2.24e-15 ***
## category_code_LT01_5_count 0.91694 0.06157 14.894 < 2e-16 ***
## category_code_LT01_11_count 0.39557 0.11294 3.502 0.000503 ***
## category_code_LT01_16_count 0.74079 1.16467 0.636 0.525041
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6264
## F-statistic: 167.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617344413206155
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7639 0.0470 0.8730 3.4321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99133 0.08771 113.919 < 2e-16 ***
## category_code_LT01_3_count 0.38042 0.11327 3.359 0.000844 ***
## category_code_LT01_4_count 0.91347 0.08327 10.970 < 2e-16 ***
## category_code_LT01_5_count 0.92038 0.06255 14.715 < 2e-16 ***
## category_code_LT01_12_count 0.14417 0.20634 0.699 0.485083
## category_code_LT01_13_count 0.12005 0.24477 0.490 0.624027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617371440634914
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0144 -0.7693 0.0520 0.8713 3.4329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99337 0.08777 113.856 < 2e-16 ***
## category_code_LT01_3_count 0.38417 0.11337 3.389 0.000758 ***
## category_code_LT01_4_count 0.90973 0.08437 10.782 < 2e-16 ***
## category_code_LT01_5_count 0.91780 0.06284 14.606 < 2e-16 ***
## category_code_LT01_12_count 0.13835 0.20691 0.669 0.504034
## category_code_LT01_14_count 0.17347 0.33060 0.525 0.600021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6174
## F-statistic: 161.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.61715734226311
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0149 -0.7712 0.0357 0.8764 3.4311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.991530 0.087728 113.892 < 2e-16 ***
## category_code_LT01_3_count 0.381341 0.114295 3.336 0.000913 ***
## category_code_LT01_4_count 0.918952 0.082782 11.101 < 2e-16 ***
## category_code_LT01_5_count 0.921080 0.062563 14.723 < 2e-16 ***
## category_code_LT01_12_count 0.147023 0.206374 0.712 0.476549
## category_code_LT01_15_count 0.003727 0.759961 0.005 0.996089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6172
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617500462160083
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0147 -0.7628 0.0307 0.8804 3.4316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99196 0.08769 113.948 <2e-16 ***
## category_code_LT01_3_count 0.37187 0.11413 3.258 0.0012 **
## category_code_LT01_4_count 0.91985 0.08250 11.149 <2e-16 ***
## category_code_LT01_5_count 0.92023 0.06253 14.716 <2e-16 ***
## category_code_LT01_12_count 0.14826 0.20623 0.719 0.4725
## category_code_LT01_16_count 0.78294 1.17849 0.664 0.5068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617226373158148
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0172 -0.7624 0.0479 0.8661 3.4290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99421 0.08777 113.86 < 2e-16 ***
## category_code_LT01_3_count 0.38972 0.11297 3.45 0.000609 ***
## category_code_LT01_4_count 0.91221 0.08425 10.83 < 2e-16 ***
## category_code_LT01_5_count 0.92055 0.06265 14.69 < 2e-16 ***
## category_code_LT01_13_count 0.12489 0.24471 0.51 0.610036
## category_code_LT01_14_count 0.19114 0.32961 0.58 0.562251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6172
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.61696508937077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7749 0.0371 0.8610 3.4267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99225 0.08774 113.883 < 2e-16 ***
## category_code_LT01_3_count 0.38681 0.11393 3.395 0.000741 ***
## category_code_LT01_4_count 0.92292 0.08246 11.192 < 2e-16 ***
## category_code_LT01_5_count 0.92445 0.06233 14.832 < 2e-16 ***
## category_code_LT01_13_count 0.12517 0.24532 0.510 0.610124
## category_code_LT01_15_count 0.01589 0.76154 0.021 0.983363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617320622475232
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0177 -0.7752 0.0341 0.8640 3.4272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99268 0.08770 113.941 < 2e-16 ***
## category_code_LT01_3_count 0.37740 0.11378 3.317 0.000977 ***
## category_code_LT01_4_count 0.92375 0.08214 11.246 < 2e-16 ***
## category_code_LT01_5_count 0.92356 0.06229 14.826 < 2e-16 ***
## category_code_LT01_13_count 0.13079 0.24484 0.534 0.593445
## category_code_LT01_16_count 0.79782 1.17948 0.676 0.499096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.617024122928217
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7593 0.0374 0.8650 3.4279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99443 0.08780 113.833 < 2e-16 ***
## category_code_LT01_3_count 0.39122 0.11397 3.433 0.000648 ***
## category_code_LT01_4_count 0.91828 0.08366 10.976 < 2e-16 ***
## category_code_LT01_5_count 0.92132 0.06266 14.702 < 2e-16 ***
## category_code_LT01_14_count 0.19120 0.32974 0.580 0.562292
## category_code_LT01_15_count -0.01687 0.75998 -0.022 0.982299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617387021816729
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.7645 0.0336 0.8640 3.4285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99499 0.08776 113.891 < 2e-16 ***
## category_code_LT01_3_count 0.38125 0.11380 3.350 0.000869 ***
## category_code_LT01_4_count 0.91858 0.08343 11.011 < 2e-16 ***
## category_code_LT01_5_count 0.92033 0.06263 14.694 < 2e-16 ***
## category_code_LT01_14_count 0.20086 0.32985 0.609 0.542849
## category_code_LT01_16_count 0.80632 1.17973 0.683 0.494626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6174
## F-statistic: 161.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.61709887776991
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0186 -0.7775 0.0308 0.8602 3.4260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99292 0.08773 113.908 < 2e-16 ***
## category_code_LT01_3_count 0.37864 0.11484 3.297 0.00105 **
## category_code_LT01_4_count 0.92984 0.08157 11.400 < 2e-16 ***
## category_code_LT01_5_count 0.92445 0.06231 14.837 < 2e-16 ***
## category_code_LT01_15_count 0.01266 0.76054 0.017 0.98673
## category_code_LT01_16_count 0.77601 1.18021 0.658 0.51116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.601183688089584
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0699 -0.8255 0.0647 0.9275 3.7711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.02988 0.08945 112.134 < 2e-16 ***
## category_code_LT01_3_count 0.53232 0.11244 4.734 2.88e-06 ***
## category_code_LT01_5_count 0.94514 0.06354 14.875 < 2e-16 ***
## category_code_LT01_6_count 0.62064 0.15301 4.056 5.80e-05 ***
## category_code_LT01_7_count 0.61781 0.15974 3.868 0.000125 ***
## category_code_LT01_11_count 0.57110 0.11348 5.033 6.80e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 492 degrees of freedom
## Multiple R-squared: 0.6052, Adjusted R-squared: 0.6012
## F-statistic: 150.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.625540532690894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0089 -0.7684 0.0136 0.9141 4.0512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99065 0.08684 115.045 < 2e-16 ***
## category_code_LT01_4_count 0.87042 0.07951 10.947 < 2e-16 ***
## category_code_LT01_5_count 0.91381 0.06253 14.615 < 2e-16 ***
## category_code_LT01_6_count 0.50341 0.14910 3.376 0.000793 ***
## category_code_LT01_7_count 0.51990 0.15278 3.403 0.000721 ***
## category_code_LT01_8_count -0.19436 0.27311 -0.712 0.477011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6255
## F-statistic: 167 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.627735909557418
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9918 -0.7564 0.0018 0.9150 4.0585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98339 0.08660 115.279 < 2e-16 ***
## category_code_LT01_4_count 0.84976 0.08008 10.611 < 2e-16 ***
## category_code_LT01_5_count 0.89950 0.06184 14.546 < 2e-16 ***
## category_code_LT01_6_count 0.47965 0.14893 3.221 0.00136 **
## category_code_LT01_7_count 0.48564 0.15317 3.171 0.00162 **
## category_code_LT01_9_count 0.41327 0.22377 1.847 0.06537 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 492 degrees of freedom
## Multiple R-squared: 0.6315, Adjusted R-squared: 0.6277
## F-statistic: 168.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.625579839816333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9850 -0.7546 0.0193 0.9100 3.9780
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97097 0.09006 110.720 < 2e-16 ***
## category_code_LT01_4_count 0.86674 0.07968 10.877 < 2e-16 ***
## category_code_LT01_5_count 0.90761 0.06187 14.669 < 2e-16 ***
## category_code_LT01_6_count 0.48028 0.15114 3.178 0.00158 **
## category_code_LT01_7_count 0.50670 0.15326 3.306 0.00101 **
## category_code_LT01_10_count 0.08458 0.11321 0.747 0.45536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6256
## F-statistic: 167.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.631711646853191
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0038 -0.7473 0.0276 0.9658 3.8152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99258 0.08610 116.063 < 2e-16 ***
## category_code_LT01_4_count 0.73531 0.09117 8.066 5.6e-15 ***
## category_code_LT01_5_count 0.90362 0.06137 14.723 < 2e-16 ***
## category_code_LT01_6_count 0.41359 0.15057 2.747 0.00624 **
## category_code_LT01_7_count 0.39604 0.15682 2.526 0.01187 *
## category_code_LT01_11_count 0.33771 0.11411 2.960 0.00323 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 492 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.6317
## F-statistic: 171.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.625362461628698
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0003 -0.7522 0.0029 0.9245 4.0535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98840 0.08683 115.032 < 2e-16 ***
## category_code_LT01_4_count 0.86387 0.08061 10.717 < 2e-16 ***
## category_code_LT01_5_count 0.90466 0.06211 14.565 < 2e-16 ***
## category_code_LT01_6_count 0.48915 0.15029 3.255 0.001213 **
## category_code_LT01_7_count 0.51523 0.15276 3.373 0.000803 ***
## category_code_LT01_12_count 0.10705 0.20511 0.522 0.601983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6254
## F-statistic: 166.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.625189851042125
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0024 -0.7579 0.0090 0.9200 4.0531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98877 0.08685 115.014 < 2e-16 ***
## category_code_LT01_4_count 0.86881 0.08005 10.853 < 2e-16 ***
## category_code_LT01_5_count 0.90712 0.06192 14.651 < 2e-16 ***
## category_code_LT01_6_count 0.49968 0.14907 3.352 0.000864 ***
## category_code_LT01_7_count 0.51254 0.15388 3.331 0.000931 ***
## category_code_LT01_13_count 0.05211 0.24387 0.214 0.830879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.625348576500067
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0019 -0.7429 0.0171 0.9177 4.0514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99044 0.08688 114.988 < 2e-16 ***
## category_code_LT01_4_count 0.86200 0.08139 10.591 < 2e-16 ***
## category_code_LT01_5_count 0.90398 0.06226 14.519 < 2e-16 ***
## category_code_LT01_6_count 0.50599 0.14961 3.382 0.000777 ***
## category_code_LT01_7_count 0.51089 0.15315 3.336 0.000914 ***
## category_code_LT01_14_count 0.16531 0.32793 0.504 0.614412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6253
## F-statistic: 166.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.625246458017848
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0031 -0.7598 0.0083 0.9105 4.0529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98896 0.08684 115.027 < 2e-16 ***
## category_code_LT01_4_count 0.86676 0.08036 10.786 < 2e-16 ***
## category_code_LT01_5_count 0.90778 0.06191 14.664 < 2e-16 ***
## category_code_LT01_6_count 0.49645 0.14928 3.326 0.000948 ***
## category_code_LT01_7_count 0.51786 0.15282 3.389 0.000759 ***
## category_code_LT01_15_count 0.25858 0.74651 0.346 0.729197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.6264231712426
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0001 -0.7532 0.0219 0.9189 4.0536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98830 0.08670 115.199 < 2e-16 ***
## category_code_LT01_4_count 0.86200 0.07970 10.815 < 2e-16 ***
## category_code_LT01_5_count 0.90442 0.06184 14.624 < 2e-16 ***
## category_code_LT01_6_count 0.51107 0.14909 3.428 0.000659 ***
## category_code_LT01_7_count 0.51553 0.15253 3.380 0.000783 ***
## category_code_LT01_16_count 1.49594 1.15756 1.292 0.196853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6264
## F-statistic: 167.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.62047557334787
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0046 -0.7747 0.0352 0.9420 4.0567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98514 0.08748 114.148 < 2e-16 ***
## category_code_LT01_4_count 0.94887 0.07446 12.744 < 2e-16 ***
## category_code_LT01_5_count 0.91546 0.06301 14.528 < 2e-16 ***
## category_code_LT01_6_count 0.48894 0.15047 3.249 0.00124 **
## category_code_LT01_8_count -0.18404 0.27494 -0.669 0.50358
## category_code_LT01_9_count 0.49531 0.22470 2.204 0.02796 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6205
## F-statistic: 163.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.617556107935007
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9930 -0.7875 0.0137 0.9028 3.9458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96671 0.09102 109.496 < 2e-16 ***
## category_code_LT01_4_count 0.97343 0.07367 13.213 < 2e-16 ***
## category_code_LT01_5_count 0.92530 0.06310 14.664 < 2e-16 ***
## category_code_LT01_6_count 0.48616 0.15285 3.181 0.00156 **
## category_code_LT01_8_count -0.16994 0.27590 -0.616 0.53821
## category_code_LT01_10_count 0.11773 0.11402 1.033 0.30232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6176
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.627129109055393
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0154 -0.7577 0.0221 0.9585 3.7615
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99552 0.08666 115.339 < 2e-16 ***
## category_code_LT01_4_count 0.78560 0.08963 8.765 < 2e-16 ***
## category_code_LT01_5_count 0.91616 0.06235 14.694 < 2e-16 ***
## category_code_LT01_6_count 0.40507 0.15157 2.672 0.007780 **
## category_code_LT01_8_count -0.13711 0.27250 -0.503 0.615077
## category_code_LT01_11_count 0.41095 0.11092 3.705 0.000236 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6271
## F-statistic: 168.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.61699665525537
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0152 -0.7936 0.0123 0.9325 4.0508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99113 0.08783 113.755 < 2e-16 ***
## category_code_LT01_4_count 0.97378 0.07456 13.061 < 2e-16 ***
## category_code_LT01_5_count 0.92220 0.06335 14.557 < 2e-16 ***
## category_code_LT01_6_count 0.50132 0.15203 3.298 0.00105 **
## category_code_LT01_8_count -0.17039 0.27621 -0.617 0.53761
## category_code_LT01_12_count 0.12203 0.20747 0.588 0.55669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.616986367122211
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0165 -0.7808 0.0256 0.9179 4.0507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99121 0.08783 113.756 < 2e-16 ***
## category_code_LT01_4_count 0.97438 0.07442 13.093 < 2e-16 ***
## category_code_LT01_5_count 0.92386 0.06319 14.621 < 2e-16 ***
## category_code_LT01_6_count 0.51340 0.15077 3.405 0.000715 ***
## category_code_LT01_8_count -0.15602 0.27653 -0.564 0.572865
## category_code_LT01_13_count 0.14141 0.24515 0.577 0.564330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.617165628893467
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0165 -0.7835 0.0362 0.9039 4.0479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99398 0.08786 113.747 < 2e-16 ***
## category_code_LT01_4_count 0.96700 0.07589 12.741 < 2e-16 ***
## category_code_LT01_5_count 0.91997 0.06351 14.485 < 2e-16 ***
## category_code_LT01_6_count 0.52276 0.15131 3.455 0.000598 ***
## category_code_LT01_8_count -0.16893 0.27605 -0.612 0.540851
## category_code_LT01_14_count 0.24817 0.33068 0.750 0.453314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6172
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.616780400196191
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0181 -0.7813 0.0298 0.9111 4.0502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99171 0.08785 113.735 < 2e-16 ***
## category_code_LT01_4_count 0.97909 0.07407 13.218 < 2e-16 ***
## category_code_LT01_5_count 0.92552 0.06318 14.650 < 2e-16 ***
## category_code_LT01_6_count 0.51067 0.15103 3.381 0.000779 ***
## category_code_LT01_8_count -0.16583 0.27616 -0.601 0.548445
## category_code_LT01_15_count 0.19694 0.75467 0.261 0.794230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.618095564220195
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0157 -0.7820 0.0231 0.9455 4.0507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99121 0.08770 113.925 < 2e-16 ***
## category_code_LT01_4_count 0.97264 0.07350 13.234 < 2e-16 ***
## category_code_LT01_5_count 0.92271 0.06308 14.627 < 2e-16 ***
## category_code_LT01_6_count 0.52544 0.15085 3.483 0.00054 ***
## category_code_LT01_8_count -0.18447 0.27605 -0.668 0.50430
## category_code_LT01_16_count 1.55600 1.17200 1.328 0.18491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6181
## F-statistic: 161.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.620586909445745
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9806 -0.7562 0.0540 0.9273 3.9800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96513 0.09064 109.944 < 2e-16 ***
## category_code_LT01_4_count 0.94384 0.07472 12.631 < 2e-16 ***
## category_code_LT01_5_count 0.90982 0.06235 14.591 < 2e-16 ***
## category_code_LT01_6_count 0.46624 0.15235 3.060 0.00233 **
## category_code_LT01_9_count 0.46973 0.22619 2.077 0.03835 *
## category_code_LT01_10_count 0.08804 0.11438 0.770 0.44180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6206
## F-statistic: 163.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.629599765389811
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7565 0.0402 0.9586 3.7806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98845 0.08640 115.612 < 2e-16 ***
## category_code_LT01_4_count 0.76563 0.08988 8.519 < 2e-16 ***
## category_code_LT01_5_count 0.90316 0.06163 14.654 < 2e-16 ***
## category_code_LT01_6_count 0.38613 0.15115 2.555 0.010933 *
## category_code_LT01_9_count 0.41898 0.22279 1.881 0.060618 .
## category_code_LT01_11_count 0.39358 0.11097 3.547 0.000428 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 492 degrees of freedom
## Multiple R-squared: 0.6333, Adjusted R-squared: 0.6296
## F-statistic: 170 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.620353740148
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9963 -0.7730 0.0407 0.9371 4.0588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98303 0.08746 114.142 < 2e-16 ***
## category_code_LT01_4_count 0.94150 0.07569 12.439 < 2e-16 ***
## category_code_LT01_5_count 0.90657 0.06259 14.483 < 2e-16 ***
## category_code_LT01_6_count 0.47474 0.15165 3.131 0.00185 **
## category_code_LT01_9_count 0.48882 0.22465 2.176 0.03004 *
## category_code_LT01_12_count 0.11119 0.20648 0.539 0.59046
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6204
## F-statistic: 163.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.620549263981587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9971 -0.7725 0.0338 0.9008 4.0589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98294 0.08744 114.172 < 2e-16 ***
## category_code_LT01_4_count 0.93863 0.07571 12.397 < 2e-16 ***
## category_code_LT01_5_count 0.90792 0.06239 14.553 < 2e-16 ***
## category_code_LT01_6_count 0.48577 0.15035 3.231 0.00132 **
## category_code_LT01_9_count 0.49997 0.22493 2.223 0.02668 *
## category_code_LT01_13_count 0.17991 0.24400 0.737 0.46126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6205
## F-statistic: 163.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.620388377061166
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9980 -0.7750 0.0283 0.9300 4.0564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98543 0.08751 114.100 < 2e-16 ***
## category_code_LT01_4_count 0.93797 0.07678 12.217 < 2e-16 ***
## category_code_LT01_5_count 0.90551 0.06273 14.435 < 2e-16 ***
## category_code_LT01_6_count 0.49331 0.15103 3.266 0.00117 **
## category_code_LT01_9_count 0.48074 0.22526 2.134 0.03333 *
## category_code_LT01_14_count 0.19109 0.33018 0.579 0.56304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6204
## F-statistic: 163.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.620198683791501
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9990 -0.7911 0.0292 0.9139 4.0583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98358 0.08747 114.131 < 2e-16 ***
## category_code_LT01_4_count 0.94551 0.07529 12.559 < 2e-16 ***
## category_code_LT01_5_count 0.90974 0.06239 14.581 < 2e-16 ***
## category_code_LT01_6_count 0.48271 0.15066 3.204 0.00144 **
## category_code_LT01_9_count 0.49191 0.22471 2.189 0.02906 *
## category_code_LT01_15_count 0.22420 0.75140 0.298 0.76554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6202
## F-statistic: 163.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.621216571217796
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9967 -0.7852 0.0331 0.9233 4.0587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98316 0.08736 114.279 < 2e-16 ***
## category_code_LT01_4_count 0.94143 0.07464 12.612 < 2e-16 ***
## category_code_LT01_5_count 0.90692 0.06233 14.549 < 2e-16 ***
## category_code_LT01_6_count 0.49678 0.15053 3.300 0.00104 **
## category_code_LT01_9_count 0.47682 0.22467 2.122 0.03431 *
## category_code_LT01_16_count 1.38660 1.16715 1.188 0.23540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 492 degrees of freedom
## Multiple R-squared: 0.625, Adjusted R-squared: 0.6212
## F-statistic: 164 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.627537200456219
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7449 -0.0102 0.9383 3.6756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97298 0.08983 111.023 < 2e-16 ***
## category_code_LT01_4_count 0.77920 0.08976 8.681 < 2e-16 ***
## category_code_LT01_5_count 0.91159 0.06164 14.789 < 2e-16 ***
## category_code_LT01_6_count 0.37996 0.15331 2.478 0.013531 *
## category_code_LT01_10_count 0.10023 0.11259 0.890 0.373777
## category_code_LT01_11_count 0.40861 0.11091 3.684 0.000255 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6275
## F-statistic: 168.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.617486376264308
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9856 -0.7666 0.0015 0.9243 3.9506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96529 0.09101 109.499 < 2e-16 ***
## category_code_LT01_4_count 0.96585 0.07491 12.893 < 2e-16 ***
## category_code_LT01_5_count 0.91674 0.06266 14.631 < 2e-16 ***
## category_code_LT01_6_count 0.47259 0.15394 3.070 0.00226 **
## category_code_LT01_10_count 0.11469 0.11407 1.005 0.31518
## category_code_LT01_12_count 0.11158 0.20733 0.538 0.59070
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.617519613800433
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9871 -0.7698 0.0040 0.9080 3.9507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96553 0.09100 109.506 < 2e-16 ***
## category_code_LT01_4_count 0.96583 0.07476 12.919 < 2e-16 ***
## category_code_LT01_5_count 0.91861 0.06245 14.710 < 2e-16 ***
## category_code_LT01_6_count 0.48407 0.15278 3.169 0.00163 **
## category_code_LT01_10_count 0.11434 0.11407 1.002 0.31666
## category_code_LT01_13_count 0.14109 0.24472 0.577 0.56450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.617467586503103
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7682 -0.0001 0.9040 3.9593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97004 0.09145 109.021 < 2e-16 ***
## category_code_LT01_4_count 0.96364 0.07595 12.687 < 2e-16 ***
## category_code_LT01_5_count 0.91586 0.06286 14.571 < 2e-16 ***
## category_code_LT01_6_count 0.49295 0.15404 3.200 0.00146 **
## category_code_LT01_10_count 0.10244 0.11726 0.874 0.38277
## category_code_LT01_14_count 0.17512 0.33990 0.515 0.60663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6175
## F-statistic: 161.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.617286018749587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9883 -0.7765 -0.0071 0.9063 3.9498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96578 0.09105 109.448 < 2e-16 ***
## category_code_LT01_4_count 0.97134 0.07439 13.057 < 2e-16 ***
## category_code_LT01_5_count 0.91984 0.06246 14.728 < 2e-16 ***
## category_code_LT01_6_count 0.48166 0.15294 3.149 0.00174 **
## category_code_LT01_10_count 0.11500 0.11437 1.006 0.31514
## category_code_LT01_15_count 0.13511 0.75631 0.179 0.85829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.618441657040354
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9870 -0.7765 -0.0047 0.9195 3.9567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96669 0.09090 109.645 < 2e-16 ***
## category_code_LT01_4_count 0.96514 0.07387 13.065 < 2e-16 ***
## category_code_LT01_5_count 0.91673 0.06239 14.693 < 2e-16 ***
## category_code_LT01_6_count 0.49612 0.15296 3.243 0.00126 **
## category_code_LT01_10_count 0.10783 0.11409 0.945 0.34507
## category_code_LT01_16_count 1.44608 1.17210 1.234 0.21788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6184
## F-statistic: 162.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.627050151080891
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0128 -0.7368 0.0178 0.9595 3.7533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99478 0.08665 115.351 < 2e-16 ***
## category_code_LT01_4_count 0.78460 0.08962 8.755 < 2e-16 ***
## category_code_LT01_5_count 0.91345 0.06188 14.762 < 2e-16 ***
## category_code_LT01_6_count 0.40656 0.15196 2.675 0.007712 **
## category_code_LT01_11_count 0.42380 0.11469 3.695 0.000245 ***
## category_code_LT01_12_count -0.08168 0.21163 -0.386 0.699704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6271
## F-statistic: 168.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62705036025043
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0103 -0.7434 0.0266 0.9620 3.7638
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99403 0.08664 115.352 < 2e-16 ***
## category_code_LT01_4_count 0.78089 0.09012 8.665 < 2e-16 ***
## category_code_LT01_5_count 0.91093 0.06170 14.763 < 2e-16 ***
## category_code_LT01_6_count 0.40295 0.15148 2.660 0.008066 **
## category_code_LT01_11_count 0.40981 0.11111 3.688 0.000251 ***
## category_code_LT01_13_count 0.09348 0.24199 0.386 0.699440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6271
## F-statistic: 168.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.627195065711485
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0099 -0.7477 0.0310 0.9276 3.7619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99603 0.08667 115.333 < 2e-16 ***
## category_code_LT01_4_count 0.77451 0.09124 8.488 2.49e-16 ***
## category_code_LT01_5_count 0.90751 0.06206 14.624 < 2e-16 ***
## category_code_LT01_6_count 0.41005 0.15209 2.696 0.007254 **
## category_code_LT01_11_count 0.40960 0.11098 3.691 0.000249 ***
## category_code_LT01_14_count 0.19051 0.32659 0.583 0.559947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6272
## F-statistic: 168.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.62693888604128
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.7449 0.0147 0.9600 3.7619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99430 0.08665 115.341 < 2e-16 ***
## category_code_LT01_4_count 0.78421 0.08995 8.718 < 2e-16 ***
## category_code_LT01_5_count 0.91161 0.06170 14.774 < 2e-16 ***
## category_code_LT01_6_count 0.40143 0.15161 2.648 0.00836 **
## category_code_LT01_11_count 0.41220 0.11109 3.710 0.00023 ***
## category_code_LT01_15_count 0.03474 0.74578 0.047 0.96286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6269
## F-statistic: 168 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.627872746389802
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0087 -0.7397 0.0353 0.9535 3.7669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99371 0.08654 115.477 < 2e-16 ***
## category_code_LT01_4_count 0.78005 0.08961 8.705 < 2e-16 ***
## category_code_LT01_5_count 0.90910 0.06165 14.746 < 2e-16 ***
## category_code_LT01_6_count 0.41356 0.15165 2.727 0.006619 **
## category_code_LT01_11_count 0.40585 0.11093 3.659 0.000281 ***
## category_code_LT01_16_count 1.28672 1.15698 1.112 0.266620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 492 degrees of freedom
## Multiple R-squared: 0.6316, Adjusted R-squared: 0.6279
## F-statistic: 168.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.61697400212419
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7849 0.0183 0.9467 4.0526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98925 0.08780 113.773 < 2e-16 ***
## category_code_LT01_4_count 0.96625 0.07561 12.780 < 2e-16 ***
## category_code_LT01_5_count 0.91567 0.06272 14.599 < 2e-16 ***
## category_code_LT01_6_count 0.49922 0.15195 3.285 0.00109 **
## category_code_LT01_12_count 0.11410 0.20747 0.550 0.58260
## category_code_LT01_13_count 0.14516 0.24487 0.593 0.55359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.617
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617073026934626
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7813 -0.0010 0.9237 4.0501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99179 0.08784 113.746 < 2e-16 ***
## category_code_LT01_4_count 0.96074 0.07686 12.500 < 2e-16 ***
## category_code_LT01_5_count 0.91200 0.06305 14.466 < 2e-16 ***
## category_code_LT01_6_count 0.50839 0.15265 3.330 0.000933 ***
## category_code_LT01_12_count 0.10520 0.20815 0.505 0.613512
## category_code_LT01_14_count 0.22968 0.33196 0.692 0.489324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616755894516489
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0101 -0.7866 -0.0006 0.9454 4.0522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98965 0.08782 113.748 < 2e-16 ***
## category_code_LT01_4_count 0.97075 0.07539 12.877 < 2e-16 ***
## category_code_LT01_5_count 0.91690 0.06273 14.617 < 2e-16 ***
## category_code_LT01_6_count 0.49574 0.15223 3.257 0.00121 **
## category_code_LT01_12_count 0.11902 0.20747 0.574 0.56644
## category_code_LT01_15_count 0.20142 0.75481 0.267 0.78970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617989806007757
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0072 -0.7847 0.0077 0.9601 4.0529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98901 0.08768 113.923 < 2e-16 ***
## category_code_LT01_4_count 0.96485 0.07477 12.903 < 2e-16 ***
## category_code_LT01_5_count 0.91365 0.06266 14.581 < 2e-16 ***
## category_code_LT01_6_count 0.51013 0.15203 3.356 0.000853 ***
## category_code_LT01_12_count 0.11535 0.20710 0.557 0.577810
## category_code_LT01_16_count 1.50848 1.17059 1.289 0.198125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617165458503663
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0100 -0.7814 0.0190 0.9029 4.0498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99204 0.08783 113.771 < 2e-16 ***
## category_code_LT01_4_count 0.95890 0.07700 12.453 < 2e-16 ***
## category_code_LT01_5_count 0.91332 0.06288 14.524 < 2e-16 ***
## category_code_LT01_6_count 0.51984 0.15119 3.438 0.000635 ***
## category_code_LT01_13_count 0.14970 0.24469 0.612 0.540969
## category_code_LT01_14_count 0.24490 0.33063 0.741 0.459225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6172
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616805613654495
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0116 -0.7794 0.0095 0.9075 4.0521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98982 0.08781 113.761 < 2e-16 ***
## category_code_LT01_4_count 0.97023 0.07531 12.883 < 2e-16 ***
## category_code_LT01_5_count 0.91892 0.06252 14.699 < 2e-16 ***
## category_code_LT01_6_count 0.50770 0.15092 3.364 0.000828 ***
## category_code_LT01_13_count 0.15376 0.24527 0.627 0.531015
## category_code_LT01_15_count 0.22186 0.75604 0.293 0.769305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6168
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.618082089684085
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0085 -0.7787 0.0018 0.9404 4.0528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98913 0.08767 113.942 < 2e-16 ***
## category_code_LT01_4_count 0.96384 0.07467 12.908 < 2e-16 ***
## category_code_LT01_5_count 0.91542 0.06245 14.658 < 2e-16 ***
## category_code_LT01_6_count 0.52225 0.15072 3.465 0.000577 ***
## category_code_LT01_13_count 0.16020 0.24454 0.655 0.512708
## category_code_LT01_16_count 1.54065 1.17106 1.316 0.188922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6181
## F-statistic: 161.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616920339918048
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0114 -0.7820 0.0181 0.9151 4.0494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99246 0.08785 113.742 < 2e-16 ***
## category_code_LT01_4_count 0.96432 0.07663 12.585 < 2e-16 ***
## category_code_LT01_5_count 0.91473 0.06289 14.544 < 2e-16 ***
## category_code_LT01_6_count 0.51693 0.15147 3.413 0.000696 ***
## category_code_LT01_14_count 0.24317 0.33078 0.735 0.462589
## category_code_LT01_15_count 0.18363 0.75460 0.243 0.807841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6169
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.618259390276535
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0082 -0.7816 0.0191 0.9345 4.0499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99201 0.08770 113.936 < 2e-16 ***
## category_code_LT01_4_count 0.95608 0.07621 12.545 < 2e-16 ***
## category_code_LT01_5_count 0.91076 0.06283 14.496 < 2e-16 ***
## category_code_LT01_6_count 0.53221 0.15130 3.518 0.000476 ***
## category_code_LT01_14_count 0.26817 0.33063 0.811 0.417706
## category_code_LT01_16_count 1.56569 1.17181 1.336 0.182125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6183
## F-statistic: 162 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617812658913649
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0101 -0.7792 0.0019 0.9365 4.0523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98961 0.08770 113.910 < 2e-16 ***
## category_code_LT01_4_count 0.96937 0.07432 13.043 < 2e-16 ***
## category_code_LT01_5_count 0.91696 0.06246 14.682 < 2e-16 ***
## category_code_LT01_6_count 0.51886 0.15098 3.437 0.000639 ***
## category_code_LT01_15_count 0.21588 0.75383 0.286 0.774712
## category_code_LT01_16_count 1.52267 1.17114 1.300 0.194152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6178
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.620204932953105
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0328 -0.7947 -0.0254 0.8717 4.0367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00515 0.08729 114.624 < 2e-16 ***
## category_code_LT01_4_count 0.95687 0.07363 12.996 < 2e-16 ***
## category_code_LT01_5_count 0.92735 0.06276 14.776 < 2e-16 ***
## category_code_LT01_7_count 0.49430 0.15477 3.194 0.00149 **
## category_code_LT01_8_count -0.17624 0.27497 -0.641 0.52186
## category_code_LT01_9_count 0.46920 0.22554 2.080 0.03802 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6202
## F-statistic: 163.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.618177331581297
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0133 -0.7693 0.0227 0.8475 3.9010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97976 0.09094 109.742 < 2e-16 ***
## category_code_LT01_4_count 0.97128 0.07335 13.242 < 2e-16 ***
## category_code_LT01_5_count 0.93582 0.06277 14.910 < 2e-16 ***
## category_code_LT01_7_count 0.51197 0.15483 3.307 0.00101 **
## category_code_LT01_8_count -0.16609 0.27563 -0.603 0.54708
## category_code_LT01_10_count 0.14662 0.11272 1.301 0.19395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6182
## F-statistic: 161.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.626238561625066
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0390 -0.7456 0.0346 0.9257 3.7552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01186 0.08653 115.710 < 2e-16 ***
## category_code_LT01_4_count 0.80360 0.08851 9.079 < 2e-16 ***
## category_code_LT01_5_count 0.92657 0.06216 14.905 < 2e-16 ***
## category_code_LT01_7_count 0.38553 0.15802 2.440 0.015048 *
## category_code_LT01_8_count -0.13089 0.27276 -0.480 0.631548
## category_code_LT01_11_count 0.39643 0.11285 3.513 0.000484 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6262
## F-statistic: 167.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.6175857868623
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0409 -0.7879 0.0069 0.8634 4.0313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01055 0.08754 114.357 < 2e-16 ***
## category_code_LT01_4_count 0.96877 0.07467 12.975 < 2e-16 ***
## category_code_LT01_5_count 0.93115 0.06308 14.760 < 2e-16 ***
## category_code_LT01_7_count 0.52693 0.15439 3.413 0.000696 ***
## category_code_LT01_8_count -0.16843 0.27597 -0.610 0.541936
## category_code_LT01_12_count 0.19811 0.20562 0.963 0.335778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6176
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.61687909827465
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0461 -0.7651 0.0069 0.8365 4.0298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01210 0.08760 114.287 < 2e-16 ***
## category_code_LT01_4_count 0.98453 0.07315 13.459 < 2e-16 ***
## category_code_LT01_5_count 0.93655 0.06289 14.891 < 2e-16 ***
## category_code_LT01_7_count 0.52684 0.15567 3.384 0.00077 ***
## category_code_LT01_8_count -0.15639 0.27657 -0.565 0.57202
## category_code_LT01_13_count 0.03411 0.24702 0.138 0.89023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6169
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.616898759949832
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0463 -0.7911 0.0081 0.8495 4.0289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01297 0.08768 114.201 < 2e-16 ***
## category_code_LT01_4_count 0.98271 0.07403 13.274 < 2e-16 ***
## category_code_LT01_5_count 0.93552 0.06316 14.812 < 2e-16 ***
## category_code_LT01_7_count 0.52718 0.15489 3.404 0.000719 ***
## category_code_LT01_8_count -0.15948 0.27605 -0.578 0.563727
## category_code_LT01_14_count 0.06954 0.33033 0.211 0.833348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6169
## F-statistic: 161.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.617085029768527
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0467 -0.7581 0.0026 0.8554 4.0298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01213 0.08758 114.321 < 2e-16 ***
## category_code_LT01_4_count 0.97856 0.07384 13.252 < 2e-16 ***
## category_code_LT01_5_count 0.93724 0.06286 14.911 < 2e-16 ***
## category_code_LT01_7_count 0.53154 0.15452 3.440 0.000631 ***
## category_code_LT01_8_count -0.16055 0.27599 -0.582 0.561008
## category_code_LT01_15_count 0.40132 0.75349 0.533 0.594543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6171
## F-statistic: 161.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.617809603140667
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0454 -0.7579 0.0143 0.8619 4.0296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01227 0.08750 114.432 < 2e-16 ***
## category_code_LT01_4_count 0.98058 0.07269 13.490 < 2e-16 ***
## category_code_LT01_5_count 0.93524 0.06281 14.890 < 2e-16 ***
## category_code_LT01_7_count 0.52912 0.15432 3.429 0.000658 ***
## category_code_LT01_8_count -0.17400 0.27605 -0.630 0.528787
## category_code_LT01_16_count 1.29083 1.17011 1.103 0.270496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6178
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.62073654412524
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7667 -0.0158 0.8741 3.9339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97758 0.09061 110.120 <2e-16 ***
## category_code_LT01_4_count 0.94664 0.07418 12.761 <2e-16 ***
## category_code_LT01_5_count 0.92107 0.06204 14.847 <2e-16 ***
## category_code_LT01_7_count 0.47939 0.15502 3.092 0.0021 **
## category_code_LT01_9_count 0.43574 0.22698 1.920 0.0555 .
## category_code_LT01_10_count 0.11874 0.11316 1.049 0.2946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6207
## F-statistic: 163.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.628554510592841
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0226 -0.7414 0.0311 0.9111 3.7727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00431 0.08628 115.948 < 2e-16 ***
## category_code_LT01_4_count 0.78470 0.08874 8.842 < 2e-16 ***
## category_code_LT01_5_count 0.91376 0.06144 14.873 < 2e-16 ***
## category_code_LT01_7_count 0.35763 0.15801 2.263 0.02405 *
## category_code_LT01_9_count 0.40618 0.22362 1.816 0.06992 .
## category_code_LT01_11_count 0.38216 0.11279 3.388 0.00076 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 492 degrees of freedom
## Multiple R-squared: 0.6323, Adjusted R-squared: 0.6286
## F-statistic: 169.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.620511850486937
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0217 -0.7784 -0.0268 0.8923 4.0400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00192 0.08723 114.663 < 2e-16 ***
## category_code_LT01_4_count 0.94105 0.07559 12.449 < 2e-16 ***
## category_code_LT01_5_count 0.91598 0.06235 14.690 < 2e-16 ***
## category_code_LT01_7_count 0.48930 0.15466 3.164 0.00165 **
## category_code_LT01_9_count 0.46023 0.22541 2.042 0.04170 *
## category_code_LT01_12_count 0.18416 0.20475 0.899 0.36884
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6205
## F-statistic: 163.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.619967538147897
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0264 -0.7838 -0.0227 0.8512 4.0387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00322 0.08728 114.614 < 2e-16 ***
## category_code_LT01_4_count 0.95348 0.07428 12.836 < 2e-16 ***
## category_code_LT01_5_count 0.92094 0.06212 14.825 < 2e-16 ***
## category_code_LT01_7_count 0.48511 0.15601 3.110 0.00198 **
## category_code_LT01_9_count 0.46994 0.22608 2.079 0.03817 *
## category_code_LT01_13_count 0.07908 0.24617 0.321 0.74816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.62
## F-statistic: 163.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619892414127504
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7888 -0.0271 0.8845 4.0381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00376 0.08737 114.500 < 2e-16 ***
## category_code_LT01_4_count 0.95553 0.07488 12.760 < 2e-16 ***
## category_code_LT01_5_count 0.92100 0.06239 14.763 < 2e-16 ***
## category_code_LT01_7_count 0.49070 0.15508 3.164 0.00165 **
## category_code_LT01_9_count 0.46371 0.22596 2.052 0.04068 *
## category_code_LT01_14_count 0.02541 0.32964 0.077 0.93859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 492 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6199
## F-statistic: 163.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.620124103612012
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7839 -0.0363 0.8685 4.0385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00335 0.08726 114.643 < 2e-16 ***
## category_code_LT01_4_count 0.94905 0.07488 12.674 < 2e-16 ***
## category_code_LT01_5_count 0.92181 0.06209 14.846 < 2e-16 ***
## category_code_LT01_7_count 0.49346 0.15476 3.189 0.00152 **
## category_code_LT01_9_count 0.46633 0.22548 2.068 0.03915 *
## category_code_LT01_15_count 0.41517 0.75050 0.553 0.58038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 492 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6201
## F-statistic: 163.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.62063019000512
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0260 -0.7825 -0.0276 0.8691 4.0383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00356 0.08720 114.722 < 2e-16 ***
## category_code_LT01_4_count 0.95260 0.07370 12.926 < 2e-16 ***
## category_code_LT01_5_count 0.91983 0.06207 14.819 < 2e-16 ***
## category_code_LT01_7_count 0.49170 0.15462 3.180 0.00156 **
## category_code_LT01_9_count 0.45440 0.22556 2.015 0.04450 *
## category_code_LT01_16_count 1.14371 1.16561 0.981 0.32697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 492 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6206
## F-statistic: 163.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.627031263093897
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7676 0.0475 0.8998 3.6494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98263 0.08986 111.093 < 2e-16 ***
## category_code_LT01_4_count 0.79292 0.08879 8.931 < 2e-16 ***
## category_code_LT01_5_count 0.92117 0.06140 15.004 < 2e-16 ***
## category_code_LT01_7_count 0.36975 0.15814 2.338 0.019780 *
## category_code_LT01_10_count 0.12599 0.11152 1.130 0.259114
## category_code_LT01_11_count 0.39167 0.11283 3.472 0.000563 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 492 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.627
## F-statistic: 168.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.618495000258434
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0038 -0.7664 0.0034 0.8741 3.9099
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97794 0.09087 109.799 < 2e-16 ***
## category_code_LT01_4_count 0.95587 0.07528 12.698 < 2e-16 ***
## category_code_LT01_5_count 0.92477 0.06235 14.833 < 2e-16 ***
## category_code_LT01_7_count 0.50728 0.15471 3.279 0.00112 **
## category_code_LT01_10_count 0.14024 0.11279 1.243 0.21433
## category_code_LT01_12_count 0.18068 0.20550 0.879 0.37970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6185
## F-statistic: 162.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.617912150222803
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7660 0.0237 0.8604 3.9043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97842 0.09094 109.723 < 2e-16 ***
## category_code_LT01_4_count 0.96965 0.07385 13.129 < 2e-16 ***
## category_code_LT01_5_count 0.93001 0.06210 14.976 < 2e-16 ***
## category_code_LT01_7_count 0.50647 0.15589 3.249 0.00124 **
## category_code_LT01_10_count 0.14488 0.11276 1.285 0.19945
## category_code_LT01_13_count 0.03600 0.24626 0.146 0.88384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6179
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.617899165395834
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0077 -0.7672 0.0198 0.8673 3.9027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97779 0.09140 109.167 < 2e-16 ***
## category_code_LT01_4_count 0.97172 0.07440 13.061 < 2e-16 ***
## category_code_LT01_5_count 0.93060 0.06239 14.916 < 2e-16 ***
## category_code_LT01_7_count 0.50971 0.15504 3.288 0.00108 **
## category_code_LT01_10_count 0.14688 0.11530 1.274 0.20331
## category_code_LT01_14_count -0.02301 0.33739 -0.068 0.94566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6179
## F-statistic: 161.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.618030186821983
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0091 -0.7688 -0.0147 0.8537 3.9076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97925 0.09095 109.722 < 2e-16 ***
## category_code_LT01_4_count 0.96566 0.07443 12.975 < 2e-16 ***
## category_code_LT01_5_count 0.93051 0.06208 14.988 < 2e-16 ***
## category_code_LT01_7_count 0.51124 0.15488 3.301 0.00103 **
## category_code_LT01_10_count 0.14116 0.11314 1.248 0.21275
## category_code_LT01_15_count 0.31454 0.75533 0.416 0.67728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.618
## F-statistic: 161.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.61868666793296
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0080 -0.7692 0.0109 0.8698 3.9090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97963 0.09086 109.838 < 2e-16 ***
## category_code_LT01_4_count 0.96668 0.07342 13.167 < 2e-16 ***
## category_code_LT01_5_count 0.92833 0.06205 14.961 < 2e-16 ***
## category_code_LT01_7_count 0.50928 0.15466 3.293 0.00106 **
## category_code_LT01_10_count 0.13947 0.11276 1.237 0.21672
## category_code_LT01_16_count 1.18087 1.16880 1.010 0.31284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 492 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6187
## F-statistic: 162.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626064295001886
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.7434 0.0422 0.9291 3.7547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.010610 0.086523 115.699 < 2e-16 ***
## category_code_LT01_4_count 0.802549 0.088554 9.063 < 2e-16 ***
## category_code_LT01_5_count 0.922253 0.061728 14.941 < 2e-16 ***
## category_code_LT01_7_count 0.382342 0.158188 2.417 0.016012 *
## category_code_LT01_11_count 0.398965 0.117463 3.397 0.000738 ***
## category_code_LT01_12_count -0.006201 0.211530 -0.029 0.976624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6261
## F-statistic: 167.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626067320533028
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0345 -0.7432 0.0449 0.9302 3.7556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01052 0.08650 115.723 < 2e-16 ***
## category_code_LT01_4_count 0.80197 0.08877 9.034 < 2e-16 ***
## category_code_LT01_5_count 0.92201 0.06148 14.996 < 2e-16 ***
## category_code_LT01_7_count 0.38141 0.15886 2.401 0.016726 *
## category_code_LT01_11_count 0.39777 0.11288 3.524 0.000465 ***
## category_code_LT01_13_count 0.01695 0.24368 0.070 0.944563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6261
## F-statistic: 167.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626086330406997
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0345 -0.7452 0.0461 0.9193 3.7549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01118 0.08658 115.634 < 2e-16 ***
## category_code_LT01_4_count 0.80009 0.08955 8.934 < 2e-16 ***
## category_code_LT01_5_count 0.92103 0.06177 14.910 < 2e-16 ***
## category_code_LT01_7_count 0.38081 0.15827 2.406 0.016494 *
## category_code_LT01_11_count 0.39783 0.11283 3.526 0.000462 ***
## category_code_LT01_14_count 0.05639 0.32634 0.173 0.862893
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6261
## F-statistic: 167.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.626117596553103
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.7422 0.0428 0.9257 3.7571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01052 0.08650 115.733 < 2e-16 ***
## category_code_LT01_4_count 0.79994 0.08899 8.989 < 2e-16 ***
## category_code_LT01_5_count 0.92233 0.06147 15.004 < 2e-16 ***
## category_code_LT01_7_count 0.38444 0.15808 2.432 0.015371 *
## category_code_LT01_11_count 0.39573 0.11315 3.498 0.000512 ***
## category_code_LT01_15_count 0.19894 0.74662 0.266 0.789998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 492 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6261
## F-statistic: 167.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.626733307486493
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0334 -0.7370 0.0530 0.9218 3.7586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01050 0.08643 115.828 < 2e-16 ***
## category_code_LT01_4_count 0.80010 0.08845 9.045 < 2e-16 ***
## category_code_LT01_5_count 0.92044 0.06144 14.981 < 2e-16 ***
## category_code_LT01_7_count 0.38369 0.15780 2.431 0.015393 *
## category_code_LT01_11_count 0.39359 0.11283 3.488 0.000529 ***
## category_code_LT01_16_count 1.08601 1.15593 0.940 0.347929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 492 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.6267
## F-statistic: 167.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617313213520451
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0352 -0.7766 0.0193 0.8552 4.0330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00885 0.08753 114.349 < 2e-16 ***
## category_code_LT01_4_count 0.96741 0.07514 12.875 < 2e-16 ***
## category_code_LT01_5_count 0.92540 0.06245 14.819 < 2e-16 ***
## category_code_LT01_7_count 0.52123 0.15547 3.353 0.000863 ***
## category_code_LT01_12_count 0.19271 0.20563 0.937 0.349150
## category_code_LT01_13_count 0.03638 0.24650 0.148 0.882725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617310102368337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0353 -0.7843 0.0202 0.8755 4.0325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00942 0.08761 114.248 < 2e-16 ***
## category_code_LT01_4_count 0.96687 0.07583 12.750 < 2e-16 ***
## category_code_LT01_5_count 0.92479 0.06270 14.749 < 2e-16 ***
## category_code_LT01_7_count 0.52253 0.15474 3.377 0.000791 ***
## category_code_LT01_12_count 0.19149 0.20613 0.929 0.353373
## category_code_LT01_14_count 0.04415 0.33106 0.133 0.893967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6173
## F-statistic: 161.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617520833625359
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0355 -0.7735 0.0148 0.8710 4.0331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00882 0.08750 114.381 < 2e-16 ***
## category_code_LT01_4_count 0.96126 0.07592 12.662 < 2e-16 ***
## category_code_LT01_5_count 0.92590 0.06242 14.832 < 2e-16 ***
## category_code_LT01_7_count 0.52601 0.15437 3.407 0.00071 ***
## category_code_LT01_12_count 0.19490 0.20551 0.948 0.34341
## category_code_LT01_15_count 0.40474 0.75306 0.537 0.59119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.618188153833179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0339 -0.7801 0.0249 0.8740 4.0330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00885 0.08743 114.481 < 2e-16 ***
## category_code_LT01_4_count 0.96359 0.07475 12.890 < 2e-16 ***
## category_code_LT01_5_count 0.92355 0.06240 14.801 < 2e-16 ***
## category_code_LT01_7_count 0.52337 0.15419 3.394 0.000744 ***
## category_code_LT01_12_count 0.19321 0.20532 0.941 0.347168
## category_code_LT01_16_count 1.25222 1.16807 1.072 0.284226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6182
## F-statistic: 161.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616662852213546
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0407 -0.7630 -0.0010 0.8408 4.0306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01127 0.08766 114.203 < 2e-16 ***
## category_code_LT01_4_count 0.98075 0.07457 13.151 < 2e-16 ***
## category_code_LT01_5_count 0.92992 0.06250 14.878 < 2e-16 ***
## category_code_LT01_7_count 0.52110 0.15598 3.341 0.000899 ***
## category_code_LT01_13_count 0.04325 0.24662 0.175 0.860852
## category_code_LT01_14_count 0.06773 0.33043 0.205 0.837685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6205, Adjusted R-squared: 0.6167
## F-statistic: 160.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616854360905638
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0410 -0.7516 0.0029 0.8388 4.0315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01042 0.08756 114.322 <2e-16 ***
## category_code_LT01_4_count 0.97620 0.07447 13.109 <2e-16 ***
## category_code_LT01_5_count 0.93153 0.06218 14.981 <2e-16 ***
## category_code_LT01_7_count 0.52485 0.15558 3.374 0.0008 ***
## category_code_LT01_13_count 0.05063 0.24698 0.205 0.8377
## category_code_LT01_15_count 0.40514 0.75499 0.537 0.5918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6169
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617535267187884
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0393 -0.7543 0.0022 0.8384 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01044 0.08749 114.424 < 2e-16 ***
## category_code_LT01_4_count 0.97831 0.07325 13.357 < 2e-16 ***
## category_code_LT01_5_count 0.92911 0.06215 14.949 < 2e-16 ***
## category_code_LT01_7_count 0.52210 0.15542 3.359 0.000842 ***
## category_code_LT01_13_count 0.05176 0.24647 0.210 0.833760
## category_code_LT01_16_count 1.26222 1.16974 1.079 0.281089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6175
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616851659041435
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.7564 0.0012 0.8521 4.0306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01125 0.08764 114.233 < 2e-16 ***
## category_code_LT01_4_count 0.97535 0.07522 12.967 < 2e-16 ***
## category_code_LT01_5_count 0.93056 0.06248 14.894 < 2e-16 ***
## category_code_LT01_7_count 0.52647 0.15487 3.399 0.00073 ***
## category_code_LT01_14_count 0.06486 0.33036 0.196 0.84443
## category_code_LT01_15_count 0.39398 0.75373 0.523 0.60142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 492 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6169
## F-statistic: 161 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617551807365614
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0394 -0.7604 0.0037 0.8624 4.0304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01148 0.08756 114.340 < 2e-16 ***
## category_code_LT01_4_count 0.97643 0.07416 13.166 < 2e-16 ***
## category_code_LT01_5_count 0.92777 0.06246 14.855 < 2e-16 ***
## category_code_LT01_7_count 0.52321 0.15468 3.382 0.000776 ***
## category_code_LT01_14_count 0.08448 0.33041 0.256 0.798297
## category_code_LT01_16_count 1.26839 1.17041 1.084 0.279025
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 492 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6176
## F-statistic: 161.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617740250062746
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0398 -0.7509 0.0045 0.8624 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01046 0.08746 114.457 < 2e-16 ***
## category_code_LT01_4_count 0.97262 0.07395 13.152 < 2e-16 ***
## category_code_LT01_5_count 0.92975 0.06213 14.965 < 2e-16 ***
## category_code_LT01_7_count 0.52813 0.15431 3.423 0.000672 ***
## category_code_LT01_15_count 0.41790 0.75306 0.555 0.579193
## category_code_LT01_16_count 1.27130 1.16917 1.087 0.277416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 492 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6177
## F-statistic: 161.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.613619285674102
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0074 -0.7846 -0.0065 0.9059 3.9080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97353 0.09146 109.045 <2e-16 ***
## category_code_LT01_4_count 1.04357 0.06797 15.353 <2e-16 ***
## category_code_LT01_5_count 0.93631 0.06323 14.808 <2e-16 ***
## category_code_LT01_8_count -0.15783 0.27728 -0.569 0.5695
## category_code_LT01_9_count 0.50997 0.22806 2.236 0.0258 *
## category_code_LT01_10_count 0.14594 0.11392 1.281 0.2008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 492 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.6136
## F-statistic: 158.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.624847088381565
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0311 -0.7680 0.0330 0.9373 3.7277
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00591 0.08675 115.339 < 2e-16 ***
## category_code_LT01_4_count 0.82647 0.08739 9.457 < 2e-16 ***
## category_code_LT01_5_count 0.92376 0.06239 14.807 < 2e-16 ***
## category_code_LT01_8_count -0.12521 0.27325 -0.458 0.6470
## category_code_LT01_9_count 0.45393 0.22402 2.026 0.0433 *
## category_code_LT01_11_count 0.44480 0.10979 4.052 5.91e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6248
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.613055260342051
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0341 -0.7933 0.0124 0.9386 4.0382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00371 0.08812 113.524 <2e-16 ***
## category_code_LT01_4_count 1.04164 0.06932 15.026 <2e-16 ***
## category_code_LT01_5_count 0.93124 0.06355 14.654 <2e-16 ***
## category_code_LT01_8_count -0.16074 0.27761 -0.579 0.5629
## category_code_LT01_9_count 0.54285 0.22636 2.398 0.0168 *
## category_code_LT01_12_count 0.19857 0.20686 0.960 0.3376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.6131
## F-statistic: 158.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.612699914429394
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0377 -0.7942 0.0170 0.9307 4.0372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00465 0.08815 113.496 <2e-16 ***
## category_code_LT01_4_count 1.04933 0.06842 15.337 <2e-16 ***
## category_code_LT01_5_count 0.93512 0.06336 14.760 <2e-16 ***
## category_code_LT01_8_count -0.14047 0.27799 -0.505 0.6136
## category_code_LT01_9_count 0.55641 0.22674 2.454 0.0145 *
## category_code_LT01_13_count 0.16912 0.24689 0.685 0.4937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6166, Adjusted R-squared: 0.6127
## F-statistic: 158.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.612393194899172
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0395 -0.7960 0.0152 0.9077 4.0355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00639 0.08827 113.364 <2e-16 ***
## category_code_LT01_4_count 1.05441 0.06883 15.320 <2e-16 ***
## category_code_LT01_5_count 0.93516 0.06362 14.700 <2e-16 ***
## category_code_LT01_8_count -0.15208 0.27769 -0.548 0.584
## category_code_LT01_9_count 0.54348 0.22702 2.394 0.017 *
## category_code_LT01_14_count 0.09371 0.33224 0.282 0.778
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6163, Adjusted R-squared: 0.6124
## F-statistic: 158 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.61251239113514
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0397 -0.7948 0.0126 0.9268 4.0367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00521 0.08817 113.481 <2e-16 ***
## category_code_LT01_4_count 1.05259 0.06826 15.420 <2e-16 ***
## category_code_LT01_5_count 0.93726 0.06333 14.800 <2e-16 ***
## category_code_LT01_8_count -0.15270 0.27764 -0.550 0.5826
## category_code_LT01_9_count 0.54954 0.22648 2.426 0.0156 *
## category_code_LT01_15_count 0.36417 0.75782 0.481 0.6311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6164, Adjusted R-squared: 0.6125
## F-statistic: 158.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.613107870468456
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0389 -0.7951 0.0045 0.9379 4.0364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00551 0.08810 113.571 <2e-16 ***
## category_code_LT01_4_count 1.05486 0.06709 15.724 <2e-16 ***
## category_code_LT01_5_count 0.93565 0.06328 14.785 <2e-16 ***
## category_code_LT01_8_count -0.16453 0.27774 -0.592 0.554
## category_code_LT01_9_count 0.53763 0.22651 2.374 0.018 *
## category_code_LT01_16_count 1.17173 1.17850 0.994 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.617, Adjusted R-squared: 0.6131
## F-statistic: 158.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.623019852502819
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0113 -0.7476 0.0348 0.9326 3.5839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98047 0.09036 110.457 < 2e-16 ***
## category_code_LT01_4_count 0.83671 0.08741 9.572 < 2e-16 ***
## category_code_LT01_5_count 0.93187 0.06238 14.938 < 2e-16 ***
## category_code_LT01_8_count -0.11400 0.27381 -0.416 0.677
## category_code_LT01_10_count 0.14589 0.11186 1.304 0.193
## category_code_LT01_11_count 0.45767 0.10974 4.171 3.59e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.623
## F-statistic: 165.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.610381644564625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.7797 0.0244 0.9262 3.8776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97356 0.09185 108.590 <2e-16 ***
## category_code_LT01_4_count 1.06270 0.06867 15.476 <2e-16 ***
## category_code_LT01_5_count 0.94160 0.06359 14.808 <2e-16 ***
## category_code_LT01_8_count -0.14781 0.27848 -0.531 0.596
## category_code_LT01_10_count 0.17358 0.11358 1.528 0.127
## category_code_LT01_12_count 0.19385 0.20779 0.933 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6104
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.60988826676679
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7774 0.0181 0.9307 3.8733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97402 0.09190 108.528 <2e-16 ***
## category_code_LT01_4_count 1.07299 0.06756 15.883 <2e-16 ***
## category_code_LT01_5_count 0.94603 0.06339 14.924 <2e-16 ***
## category_code_LT01_8_count -0.13041 0.27895 -0.468 0.640
## category_code_LT01_10_count 0.17712 0.11356 1.560 0.119
## category_code_LT01_13_count 0.12304 0.24754 0.497 0.619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.6099
## F-statistic: 156.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.609702004800266
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0159 -0.7858 0.0202 0.9242 3.8733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97500 0.09239 107.960 <2e-16 ***
## category_code_LT01_4_count 1.07762 0.06791 15.869 <2e-16 ***
## category_code_LT01_5_count 0.94645 0.06368 14.863 <2e-16 ***
## category_code_LT01_8_count -0.13882 0.27856 -0.498 0.618
## category_code_LT01_10_count 0.17623 0.11623 1.516 0.130
## category_code_LT01_14_count 0.03750 0.34052 0.110 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6097
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.609769013655282
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0162 -0.7827 0.0200 0.9262 3.8739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97459 0.09194 108.492 <2e-16 ***
## category_code_LT01_4_count 1.07558 0.06741 15.955 <2e-16 ***
## category_code_LT01_5_count 0.94745 0.06337 14.952 <2e-16 ***
## category_code_LT01_8_count -0.13933 0.27853 -0.500 0.617
## category_code_LT01_10_count 0.17602 0.11392 1.545 0.123
## category_code_LT01_15_count 0.23719 0.76308 0.311 0.756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6098
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.610520160786075
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0157 -0.7863 0.0230 0.9348 3.8763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97532 0.09184 108.619 <2e-16 ***
## category_code_LT01_4_count 1.07499 0.06647 16.172 <2e-16 ***
## category_code_LT01_5_count 0.94572 0.06331 14.937 <2e-16 ***
## category_code_LT01_8_count -0.15248 0.27859 -0.547 0.584
## category_code_LT01_10_count 0.17322 0.11355 1.526 0.128
## category_code_LT01_16_count 1.20937 1.18267 1.023 0.307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6105
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.621733412919486
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0451 -0.7425 0.0232 0.9111 3.7014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01302 0.08706 115.011 < 2e-16 ***
## category_code_LT01_4_count 0.84972 0.08704 9.763 < 2e-16 ***
## category_code_LT01_5_count 0.93365 0.06269 14.894 < 2e-16 ***
## category_code_LT01_8_count -0.10341 0.27446 -0.377 0.706
## category_code_LT01_11_count 0.47246 0.11402 4.144 4.02e-05 ***
## category_code_LT01_12_count -0.03155 0.21262 -0.148 0.882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6217
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.621789173564034
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0436 -0.7338 0.0261 0.9052 3.7064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01255 0.08704 115.032 < 2e-16 ***
## category_code_LT01_4_count 0.84658 0.08750 9.675 < 2e-16 ***
## category_code_LT01_5_count 0.93228 0.06251 14.914 < 2e-16 ***
## category_code_LT01_8_count -0.10043 0.27462 -0.366 0.715
## category_code_LT01_11_count 0.46584 0.10983 4.242 2.65e-05 ***
## category_code_LT01_13_count 0.07504 0.24403 0.307 0.759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6218
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.621802920298579
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0441 -0.7345 0.0279 0.9147 3.7043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01399 0.08711 114.954 < 2e-16 ***
## category_code_LT01_4_count 0.84441 0.08828 9.565 < 2e-16 ***
## category_code_LT01_5_count 0.93083 0.06278 14.826 < 2e-16 ***
## category_code_LT01_8_count -0.10661 0.27420 -0.389 0.698
## category_code_LT01_11_count 0.46681 0.10968 4.256 2.49e-05 ***
## category_code_LT01_14_count 0.10983 0.32753 0.335 0.738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6218
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.621737277353436
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0444 -0.7350 0.0245 0.9133 3.7057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01276 0.08704 115.031 < 2e-16 ***
## category_code_LT01_4_count 0.84801 0.08744 9.698 < 2e-16 ***
## category_code_LT01_5_count 0.93309 0.06249 14.931 < 2e-16 ***
## category_code_LT01_8_count -0.10578 0.27422 -0.386 0.700
## category_code_LT01_11_count 0.46662 0.10989 4.246 2.6e-05 ***
## category_code_LT01_15_count 0.12338 0.75033 0.164 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6217
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.622390743083028
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0435 -0.7338 0.0268 0.9097 3.7078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01285 0.08697 115.131 < 2e-16 ***
## category_code_LT01_4_count 0.84734 0.08697 9.743 < 2e-16 ***
## category_code_LT01_5_count 0.93172 0.06244 14.921 < 2e-16 ***
## category_code_LT01_8_count -0.11832 0.27432 -0.431 0.666
## category_code_LT01_11_count 0.46349 0.10965 4.227 2.82e-05 ***
## category_code_LT01_16_count 1.09111 1.16412 0.937 0.349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6224
## F-statistic: 164.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.608743804938998
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0491 -0.8010 0.0077 0.9341 4.0304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01153 0.08855 113.066 <2e-16 ***
## category_code_LT01_4_count 1.07655 0.06864 15.683 <2e-16 ***
## category_code_LT01_5_count 0.94170 0.06375 14.772 <2e-16 ***
## category_code_LT01_8_count -0.13026 0.27949 -0.466 0.641
## category_code_LT01_12_count 0.20661 0.20806 0.993 0.321
## category_code_LT01_13_count 0.12795 0.24790 0.516 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6127, Adjusted R-squared: 0.6087
## F-statistic: 155.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.608640736480718
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0501 -0.8023 0.0038 0.9262 4.0286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01330 0.08864 112.968 <2e-16 ***
## category_code_LT01_4_count 1.07717 0.06930 15.545 <2e-16 ***
## category_code_LT01_5_count 0.94057 0.06400 14.696 <2e-16 ***
## category_code_LT01_8_count -0.13997 0.27906 -0.502 0.616
## category_code_LT01_12_count 0.20420 0.20857 0.979 0.328
## category_code_LT01_14_count 0.12352 0.33401 0.370 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6126, Adjusted R-squared: 0.6086
## F-statistic: 155.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.608696115679377
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0504 -0.8014 0.0079 0.9402 4.0301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01182 0.08855 113.066 <2e-16 ***
## category_code_LT01_4_count 1.07701 0.06877 15.662 <2e-16 ***
## category_code_LT01_5_count 0.94315 0.06373 14.800 <2e-16 ***
## category_code_LT01_8_count -0.14025 0.27904 -0.503 0.615
## category_code_LT01_12_count 0.21127 0.20798 1.016 0.310
## category_code_LT01_15_count 0.34597 0.76152 0.454 0.650
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6126, Adjusted R-squared: 0.6087
## F-statistic: 155.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.609489456794958
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0492 -0.8015 0.0035 0.9401 4.0299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01196 0.08846 113.182 <2e-16 ***
## category_code_LT01_4_count 1.07765 0.06763 15.934 <2e-16 ***
## category_code_LT01_5_count 0.94118 0.06367 14.782 <2e-16 ***
## category_code_LT01_8_count -0.15411 0.27909 -0.552 0.581
## category_code_LT01_12_count 0.21008 0.20775 1.011 0.312
## category_code_LT01_16_count 1.29909 1.18278 1.098 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6095
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.608117210747575
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0546 -0.7850 -0.0077 0.9213 4.0270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01487 0.08868 112.935 <2e-16 ***
## category_code_LT01_4_count 1.08690 0.06841 15.887 <2e-16 ***
## category_code_LT01_5_count 0.94474 0.06385 14.797 <2e-16 ***
## category_code_LT01_8_count -0.12134 0.27953 -0.434 0.664
## category_code_LT01_13_count 0.13581 0.24797 0.548 0.584
## category_code_LT01_14_count 0.14825 0.33328 0.445 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 492 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6081
## F-statistic: 155.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.608139398592454
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0551 -0.7903 -0.0072 0.9368 4.0287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01314 0.08860 113.018 <2e-16 ***
## category_code_LT01_4_count 1.08796 0.06767 16.078 <2e-16 ***
## category_code_LT01_5_count 0.94796 0.06353 14.922 <2e-16 ***
## category_code_LT01_8_count -0.12056 0.27950 -0.431 0.666
## category_code_LT01_13_count 0.14301 0.24843 0.576 0.565
## category_code_LT01_15_count 0.36272 0.76343 0.475 0.635
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 492 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6081
## F-statistic: 155.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.608946814369058
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0538 -0.7878 -0.0046 0.9370 4.0286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01328 0.08851 113.136 <2e-16 ***
## category_code_LT01_4_count 1.08863 0.06643 16.388 <2e-16 ***
## category_code_LT01_5_count 0.94590 0.06348 14.902 <2e-16 ***
## category_code_LT01_8_count -0.13457 0.27953 -0.481 0.630
## category_code_LT01_13_count 0.14416 0.24782 0.582 0.561
## category_code_LT01_16_count 1.31969 1.18415 1.114 0.266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.6089
## F-statistic: 155.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.608029407782607
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0561 -0.8031 -0.0090 0.9350 4.0267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01520 0.08868 112.930 <2e-16 ***
## category_code_LT01_4_count 1.08851 0.06835 15.925 <2e-16 ***
## category_code_LT01_5_count 0.94642 0.06382 14.830 <2e-16 ***
## category_code_LT01_8_count -0.13153 0.27911 -0.471 0.638
## category_code_LT01_14_count 0.14653 0.33334 0.440 0.660
## category_code_LT01_15_count 0.33196 0.76215 0.436 0.663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 492 degrees of freedom
## Multiple R-squared: 0.612, Adjusted R-squared: 0.608
## F-statistic: 155.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608875971065954
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0548 -0.8018 -0.0065 0.9350 4.0263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01555 0.08859 113.056 <2e-16 ***
## category_code_LT01_4_count 1.08768 0.06735 16.150 <2e-16 ***
## category_code_LT01_5_count 0.94402 0.06377 14.804 <2e-16 ***
## category_code_LT01_8_count -0.14608 0.27916 -0.523 0.601
## category_code_LT01_14_count 0.16641 0.33336 0.499 0.618
## category_code_LT01_16_count 1.32766 1.18512 1.120 0.263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6128, Adjusted R-squared: 0.6089
## F-statistic: 155.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608854526763068
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0554 -0.7932 -0.0093 0.9539 4.0282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01365 0.08851 113.131 <2e-16 ***
## category_code_LT01_4_count 1.09016 0.06640 16.418 <2e-16 ***
## category_code_LT01_5_count 0.94766 0.06345 14.936 <2e-16 ***
## category_code_LT01_8_count -0.14537 0.27915 -0.521 0.603
## category_code_LT01_15_count 0.35901 0.76159 0.471 0.638
## category_code_LT01_16_count 1.31414 1.18419 1.110 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6128, Adjusted R-squared: 0.6089
## F-statistic: 155.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.625537443678239
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0007 -0.7618 0.0384 0.9211 3.6282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97881 0.09003 110.839 < 2e-16 ***
## category_code_LT01_4_count 0.81630 0.08768 9.310 < 2e-16 ***
## category_code_LT01_5_count 0.91905 0.06164 14.911 < 2e-16 ***
## category_code_LT01_9_count 0.42065 0.22542 1.866 0.0626 .
## category_code_LT01_10_count 0.11873 0.11232 1.057 0.2910
## category_code_LT01_11_count 0.43926 0.10984 3.999 7.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 492 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6255
## F-statistic: 167 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.613971802755224
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9983 -0.7881 0.0031 0.9318 3.9165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97183 0.09140 109.106 <2e-16 ***
## category_code_LT01_4_count 1.02765 0.07011 14.657 <2e-16 ***
## category_code_LT01_5_count 0.92556 0.06281 14.736 <2e-16 ***
## category_code_LT01_9_count 0.50237 0.22788 2.205 0.0279 *
## category_code_LT01_10_count 0.13980 0.11399 1.226 0.2206
## category_code_LT01_12_count 0.18182 0.20673 0.880 0.3795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.399 on 492 degrees of freedom
## Multiple R-squared: 0.6179, Adjusted R-squared: 0.614
## F-statistic: 159.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.61371280288695
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0017 -0.7692 -0.0080 0.9191 3.9138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97243 0.09143 109.077 <2e-16 ***
## category_code_LT01_4_count 1.03407 0.06924 14.935 <2e-16 ***
## category_code_LT01_5_count 0.92966 0.06256 14.861 <2e-16 ***
## category_code_LT01_9_count 0.51528 0.22835 2.257 0.0245 *
## category_code_LT01_10_count 0.14172 0.11398 1.243 0.2143
## category_code_LT01_13_count 0.16402 0.24637 0.666 0.5059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 492 degrees of freedom
## Multiple R-squared: 0.6176, Adjusted R-squared: 0.6137
## F-statistic: 158.9 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.613364952484745
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0025 -0.7812 -0.0045 0.9143 3.9108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.972396 0.091934 108.473 <2e-16 ***
## category_code_LT01_4_count 1.042748 0.069301 15.047 <2e-16 ***
## category_code_LT01_5_count 0.930907 0.062857 14.810 <2e-16 ***
## category_code_LT01_9_count 0.505812 0.228227 2.216 0.0271 *
## category_code_LT01_10_count 0.144423 0.116483 1.240 0.2156
## category_code_LT01_14_count 0.004012 0.339208 0.012 0.9906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 492 degrees of freedom
## Multiple R-squared: 0.6173, Adjusted R-squared: 0.6134
## F-statistic: 158.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.61347009080646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0034 -0.7848 0.0021 0.9133 3.9139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97301 0.09148 109.024 <2e-16 ***
## category_code_LT01_4_count 1.03853 0.06902 15.046 <2e-16 ***
## category_code_LT01_5_count 0.93126 0.06255 14.888 <2e-16 ***
## category_code_LT01_9_count 0.50812 0.22807 2.228 0.0263 *
## category_code_LT01_10_count 0.14109 0.11435 1.234 0.2179
## category_code_LT01_15_count 0.27806 0.75969 0.366 0.7145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 492 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6135
## F-statistic: 158.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.614016659177626
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0025 -0.7751 -0.0073 0.9233 3.9146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97340 0.09140 109.121 <2e-16 ***
## category_code_LT01_4_count 1.03972 0.06802 15.286 <2e-16 ***
## category_code_LT01_5_count 0.92949 0.06252 14.866 <2e-16 ***
## category_code_LT01_9_count 0.49744 0.22802 2.182 0.0296 *
## category_code_LT01_10_count 0.14004 0.11396 1.229 0.2197
## category_code_LT01_16_count 1.07277 1.17692 0.912 0.3625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.399 on 492 degrees of freedom
## Multiple R-squared: 0.6179, Adjusted R-squared: 0.614
## F-statistic: 159.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.624705982850419
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0278 -0.7597 0.0344 0.9397 3.7244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00500 0.08675 115.334 < 2e-16 ***
## category_code_LT01_4_count 0.82556 0.08739 9.447 < 2e-16 ***
## category_code_LT01_5_count 0.92036 0.06195 14.858 < 2e-16 ***
## category_code_LT01_9_count 0.45008 0.22392 2.010 0.045 *
## category_code_LT01_11_count 0.45088 0.11409 3.952 8.88e-05 ***
## category_code_LT01_12_count -0.03340 0.21158 -0.158 0.875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6247
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62484733115233
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0260 -0.7519 0.0412 0.9170 3.7307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00435 0.08672 115.369 < 2e-16 ***
## category_code_LT01_4_count 0.82075 0.08789 9.338 < 2e-16 ***
## category_code_LT01_5_count 0.91868 0.06172 14.886 < 2e-16 ***
## category_code_LT01_9_count 0.45675 0.22433 2.036 0.0423 *
## category_code_LT01_11_count 0.44265 0.11000 4.024 6.62e-05 ***
## category_code_LT01_13_count 0.11150 0.24314 0.459 0.6467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6248
## F-statistic: 166.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.624716695354764
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7542 0.0403 0.9444 3.7276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00546 0.08681 115.254 < 2e-16 ***
## category_code_LT01_4_count 0.82242 0.08852 9.291 < 2e-16 ***
## category_code_LT01_5_count 0.91832 0.06199 14.814 < 2e-16 ***
## category_code_LT01_9_count 0.44727 0.22441 1.993 0.0468 *
## category_code_LT01_11_count 0.44554 0.10980 4.058 5.76e-05 ***
## category_code_LT01_14_count 0.06454 0.32696 0.197 0.8436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6247
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.624718878394859
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7707 0.0406 0.9361 3.7292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00467 0.08673 115.358 < 2e-16 ***
## category_code_LT01_4_count 0.82341 0.08781 9.377 < 2e-16 ***
## category_code_LT01_5_count 0.91969 0.06171 14.904 < 2e-16 ***
## category_code_LT01_9_count 0.45123 0.22397 2.015 0.0445 *
## category_code_LT01_11_count 0.44444 0.11003 4.039 6.22e-05 ***
## category_code_LT01_15_count 0.15287 0.74749 0.205 0.8380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 492 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6247
## F-statistic: 166.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.625216855024373
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0261 -0.7549 0.0458 0.9358 3.7303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00479 0.08667 115.436 < 2e-16 ***
## category_code_LT01_4_count 0.82366 0.08733 9.432 < 2e-16 ***
## category_code_LT01_5_count 0.91822 0.06168 14.887 < 2e-16 ***
## category_code_LT01_9_count 0.44217 0.22398 1.974 0.0489 *
## category_code_LT01_11_count 0.44263 0.10978 4.032 6.4e-05 ***
## category_code_LT01_16_count 0.96690 1.15932 0.834 0.4047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 492 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6252
## F-statistic: 166.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.613161538105201
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0275 -0.7913 0.0293 0.9426 4.0402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00172 0.08807 113.563 <2e-16 ***
## category_code_LT01_4_count 1.03216 0.07055 14.630 <2e-16 ***
## category_code_LT01_5_count 0.92469 0.06290 14.701 <2e-16 ***
## category_code_LT01_9_count 0.54748 0.22658 2.416 0.016 *
## category_code_LT01_12_count 0.18984 0.20679 0.918 0.359
## category_code_LT01_13_count 0.16907 0.24648 0.686 0.493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6132
## F-statistic: 158.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.6128250191633
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0289 -0.7926 0.0230 0.9463 4.0388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00304 0.08820 113.413 <2e-16 ***
## category_code_LT01_4_count 1.03826 0.07080 14.664 <2e-16 ***
## category_code_LT01_5_count 0.92476 0.06316 14.642 <2e-16 ***
## category_code_LT01_9_count 0.53537 0.22681 2.360 0.0186 *
## category_code_LT01_12_count 0.19106 0.20736 0.921 0.3573
## category_code_LT01_14_count 0.06862 0.33295 0.206 0.8368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.6128
## F-statistic: 158.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.612977375297598
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.7917 0.0246 0.9406 4.0398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00210 0.08809 113.543 <2e-16 ***
## category_code_LT01_4_count 1.03480 0.07057 14.664 <2e-16 ***
## category_code_LT01_5_count 0.92625 0.06289 14.727 <2e-16 ***
## category_code_LT01_9_count 0.54014 0.22628 2.387 0.0174 *
## category_code_LT01_12_count 0.19549 0.20676 0.945 0.3449
## category_code_LT01_15_count 0.36806 0.75737 0.486 0.6272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.613
## F-statistic: 158.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.613525630951027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0279 -0.7919 0.0195 0.9478 4.0396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00231 0.08803 113.627 <2e-16 ***
## category_code_LT01_4_count 1.03732 0.06939 14.949 <2e-16 ***
## category_code_LT01_5_count 0.92433 0.06287 14.703 <2e-16 ***
## category_code_LT01_9_count 0.52823 0.22635 2.334 0.020 *
## category_code_LT01_12_count 0.19415 0.20660 0.940 0.348
## category_code_LT01_16_count 1.13727 1.17647 0.967 0.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 492 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6135
## F-statistic: 158.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.612557763954034
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0329 -0.7939 0.0273 0.9101 4.0375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00435 0.08821 113.414 <2e-16 ***
## category_code_LT01_4_count 1.04397 0.07015 14.881 <2e-16 ***
## category_code_LT01_5_count 0.92864 0.06295 14.752 <2e-16 ***
## category_code_LT01_9_count 0.54861 0.22722 2.414 0.0161 *
## category_code_LT01_13_count 0.17591 0.24655 0.713 0.4759
## category_code_LT01_14_count 0.09080 0.33214 0.273 0.7847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6165, Adjusted R-squared: 0.6126
## F-statistic: 158.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.612711786123321
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0331 -0.7927 0.0258 0.9285 4.0387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00316 0.08811 113.534 <2e-16 ***
## category_code_LT01_4_count 1.04103 0.06977 14.920 <2e-16 ***
## category_code_LT01_5_count 0.93063 0.06264 14.857 <2e-16 ***
## category_code_LT01_9_count 0.55509 0.22667 2.449 0.0147 *
## category_code_LT01_13_count 0.18416 0.24699 0.746 0.4563
## category_code_LT01_15_count 0.39475 0.75908 0.520 0.6033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6166, Adjusted R-squared: 0.6127
## F-statistic: 158.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.613267887009262
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0319 -0.7929 0.0164 0.9398 4.0385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00336 0.08804 113.618 <2e-16 ***
## category_code_LT01_4_count 1.04383 0.06848 15.243 <2e-16 ***
## category_code_LT01_5_count 0.92861 0.06262 14.830 <2e-16 ***
## category_code_LT01_9_count 0.54275 0.22670 2.394 0.017 *
## category_code_LT01_13_count 0.18354 0.24644 0.745 0.457
## category_code_LT01_16_count 1.16457 1.17742 0.989 0.323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6133
## F-statistic: 158.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.612331308540368
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7944 0.0244 0.9089 4.0371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00479 0.08823 113.390 <2e-16 ***
## category_code_LT01_4_count 1.04774 0.07000 14.968 <2e-16 ***
## category_code_LT01_5_count 0.93044 0.06294 14.782 <2e-16 ***
## category_code_LT01_9_count 0.54107 0.22695 2.384 0.0175 *
## category_code_LT01_14_count 0.08948 0.33227 0.269 0.7878
## category_code_LT01_15_count 0.35661 0.75801 0.470 0.6382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 492 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6123
## F-statistic: 158 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.61291530238593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0334 -0.7948 0.0153 0.9167 4.0367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00520 0.08817 113.480 <2e-16 ***
## category_code_LT01_4_count 1.04899 0.06894 15.216 <2e-16 ***
## category_code_LT01_5_count 0.92811 0.06292 14.750 <2e-16 ***
## category_code_LT01_9_count 0.52812 0.22704 2.326 0.0204 *
## category_code_LT01_14_count 0.10824 0.33243 0.326 0.7449
## category_code_LT01_16_count 1.15751 1.17896 0.982 0.3267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6129
## F-statistic: 158.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.61302839083483
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0337 -0.7934 0.0134 0.9316 4.0380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00384 0.08807 113.593 <2e-16 ***
## category_code_LT01_4_count 1.04762 0.06835 15.328 <2e-16 ***
## category_code_LT01_5_count 0.93048 0.06261 14.862 <2e-16 ***
## category_code_LT01_9_count 0.53495 0.22645 2.362 0.0185 *
## category_code_LT01_15_count 0.37863 0.75753 0.500 0.6174
## category_code_LT01_16_count 1.15320 1.17763 0.979 0.3279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 492 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.613
## F-statistic: 158.5 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.622920607585906
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0086 -0.7510 0.0346 0.9337 3.5798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97979 0.09035 110.459 < 2e-16 ***
## category_code_LT01_4_count 0.83587 0.08739 9.565 < 2e-16 ***
## category_code_LT01_5_count 0.92906 0.06191 15.008 < 2e-16 ***
## category_code_LT01_10_count 0.14553 0.11190 1.301 0.194
## category_code_LT01_11_count 0.46511 0.11398 4.081 5.24e-05 ***
## category_code_LT01_12_count -0.04441 0.21220 -0.209 0.834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6229
## F-statistic: 165.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62295314904572
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0074 -0.7397 0.0409 0.9131 3.5876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97962 0.09034 110.468 < 2e-16 ***
## category_code_LT01_4_count 0.83293 0.08781 9.485 < 2e-16 ***
## category_code_LT01_5_count 0.92750 0.06168 15.037 < 2e-16 ***
## category_code_LT01_10_count 0.14384 0.11188 1.286 0.199
## category_code_LT01_11_count 0.45679 0.10991 4.156 3.82e-05 ***
## category_code_LT01_13_count 0.07148 0.24335 0.294 0.769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.623
## F-statistic: 165.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.622889359103285
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7410 0.0372 0.9340 3.5864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98008 0.09080 109.907 < 2e-16 ***
## category_code_LT01_4_count 0.83478 0.08837 9.446 < 2e-16 ***
## category_code_LT01_5_count 0.92758 0.06198 14.965 < 2e-16 ***
## category_code_LT01_10_count 0.14340 0.11448 1.253 0.211
## category_code_LT01_11_count 0.45858 0.10974 4.179 3.47e-05 ***
## category_code_LT01_14_count 0.01844 0.33473 0.055 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6229
## F-statistic: 165.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.622889522654358
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0078 -0.7422 0.0368 0.9342 3.5861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97969 0.09037 110.437 < 2e-16 ***
## category_code_LT01_4_count 0.83505 0.08773 9.518 < 2e-16 ***
## category_code_LT01_5_count 0.92799 0.06168 15.045 < 2e-16 ***
## category_code_LT01_10_count 0.14423 0.11220 1.286 0.199
## category_code_LT01_11_count 0.45827 0.10994 4.169 3.62e-05 ***
## category_code_LT01_15_count 0.04284 0.75151 0.057 0.955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 492 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6229
## F-statistic: 165.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.623448752525444
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7344 0.0425 0.9122 3.5919
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98055 0.09029 110.543 < 2e-16 ***
## category_code_LT01_4_count 0.83387 0.08733 9.548 < 2e-16 ***
## category_code_LT01_5_count 0.92647 0.06165 15.029 < 2e-16 ***
## category_code_LT01_10_count 0.14015 0.11189 1.253 0.211
## category_code_LT01_11_count 0.45511 0.10973 4.148 3.96e-05 ***
## category_code_LT01_16_count 0.99575 1.16230 0.857 0.392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 492 degrees of freedom
## Multiple R-squared: 0.6272, Adjusted R-squared: 0.6234
## F-statistic: 165.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.610356643764649
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0061 -0.7731 0.0236 0.9202 3.8820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97252 0.09182 108.606 <2e-16 ***
## category_code_LT01_4_count 1.05624 0.06967 15.160 <2e-16 ***
## category_code_LT01_5_count 0.93589 0.06292 14.875 <2e-16 ***
## category_code_LT01_10_count 0.17059 0.11361 1.502 0.134
## category_code_LT01_12_count 0.18699 0.20774 0.900 0.368
## category_code_LT01_13_count 0.12357 0.24707 0.500 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6104
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.610160120780359
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0067 -0.7740 0.0183 0.9286 3.8810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97281 0.09232 108.025 <2e-16 ***
## category_code_LT01_4_count 1.06158 0.06990 15.187 <2e-16 ***
## category_code_LT01_5_count 0.93638 0.06320 14.816 <2e-16 ***
## category_code_LT01_10_count 0.17120 0.11622 1.473 0.141
## category_code_LT01_12_count 0.18929 0.20817 0.909 0.364
## category_code_LT01_14_count 0.01521 0.34106 0.045 0.964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.61024069216197
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0073 -0.7741 0.0200 0.9331 3.8830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97303 0.09186 108.571 <2e-16 ***
## category_code_LT01_4_count 1.05832 0.06970 15.185 <2e-16 ***
## category_code_LT01_5_count 0.93690 0.06292 14.892 <2e-16 ***
## category_code_LT01_10_count 0.16919 0.11398 1.484 0.138
## category_code_LT01_12_count 0.19105 0.20772 0.920 0.358
## category_code_LT01_15_count 0.24560 0.76270 0.322 0.748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6102
## F-statistic: 156.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610946799522188
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0064 -0.7761 0.0080 0.9330 3.8853
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97360 0.09176 108.691 <2e-16 ***
## category_code_LT01_4_count 1.05798 0.06874 15.392 <2e-16 ***
## category_code_LT01_5_count 0.93478 0.06288 14.866 <2e-16 ***
## category_code_LT01_10_count 0.16656 0.11362 1.466 0.143
## category_code_LT01_12_count 0.19012 0.20750 0.916 0.360
## category_code_LT01_16_count 1.17873 1.18060 0.998 0.319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 492 degrees of freedom
## Multiple R-squared: 0.6149, Adjusted R-squared: 0.6109
## F-statistic: 157.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.609724477330306
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0110 -0.7694 0.0299 0.9150 3.8778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97401 0.09236 107.986 <2e-16 ***
## category_code_LT01_4_count 1.07031 0.06902 15.508 <2e-16 ***
## category_code_LT01_5_count 0.94081 0.06299 14.937 <2e-16 ***
## category_code_LT01_10_count 0.17305 0.11627 1.488 0.137
## category_code_LT01_13_count 0.13002 0.24718 0.526 0.599
## category_code_LT01_14_count 0.03728 0.34051 0.109 0.913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6097
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.609807097144863
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0113 -0.7686 0.0301 0.9242 3.8788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97367 0.09191 108.518 <2e-16 ***
## category_code_LT01_4_count 1.06764 0.06865 15.551 <2e-16 ***
## category_code_LT01_5_count 0.94178 0.06266 15.031 <2e-16 ***
## category_code_LT01_10_count 0.17245 0.11396 1.513 0.131
## category_code_LT01_13_count 0.13527 0.24767 0.546 0.585
## category_code_LT01_15_count 0.26060 0.76462 0.341 0.733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6098
## F-statistic: 156.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.61053188661524
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0104 -0.7696 0.0300 0.9239 3.8811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97423 0.09181 108.641 <2e-16 ***
## category_code_LT01_4_count 1.06720 0.06761 15.784 <2e-16 ***
## category_code_LT01_5_count 0.93956 0.06262 15.003 <2e-16 ***
## category_code_LT01_10_count 0.16981 0.11359 1.495 0.136
## category_code_LT01_13_count 0.13853 0.24707 0.561 0.575
## category_code_LT01_16_count 1.20070 1.18195 1.016 0.310
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6105
## F-statistic: 156.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609579649297958
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0123 -0.7700 0.0297 0.9205 3.8784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97448 0.09240 107.945 <2e-16 ***
## category_code_LT01_4_count 1.07327 0.06890 15.577 <2e-16 ***
## category_code_LT01_5_count 0.94198 0.06298 14.957 <2e-16 ***
## category_code_LT01_10_count 0.17207 0.11663 1.475 0.141
## category_code_LT01_14_count 0.03650 0.34057 0.107 0.915
## category_code_LT01_15_count 0.23410 0.76325 0.307 0.759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6096
## F-statistic: 156.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610304753504323
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0117 -0.7772 0.0291 0.9201 3.8820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97563 0.09231 108.064 <2e-16 ***
## category_code_LT01_4_count 1.07180 0.06802 15.756 <2e-16 ***
## category_code_LT01_5_count 0.93946 0.06296 14.923 <2e-16 ***
## category_code_LT01_10_count 0.16774 0.11634 1.442 0.150
## category_code_LT01_14_count 0.05648 0.34086 0.166 0.868
## category_code_LT01_16_count 1.18946 1.18373 1.005 0.315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6103
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610373294598632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0118 -0.7757 0.0281 0.9430 3.8818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97476 0.09185 108.599 <2e-16 ***
## category_code_LT01_4_count 1.07023 0.06750 15.856 <2e-16 ***
## category_code_LT01_5_count 0.94082 0.06262 15.025 <2e-16 ***
## category_code_LT01_10_count 0.16865 0.11396 1.480 0.140
## category_code_LT01_15_count 0.25758 0.76283 0.338 0.736
## category_code_LT01_16_count 1.19011 1.18205 1.007 0.315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 492 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6104
## F-statistic: 156.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.621708500194027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.7317 0.0314 0.9053 3.7030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01184 0.08702 115.046 < 2e-16 ***
## category_code_LT01_4_count 0.84558 0.08745 9.669 < 2e-16 ***
## category_code_LT01_5_count 0.92969 0.06202 14.991 < 2e-16 ***
## category_code_LT01_11_count 0.47177 0.11413 4.134 4.2e-05 ***
## category_code_LT01_12_count -0.03604 0.21244 -0.170 0.865
## category_code_LT01_13_count 0.08066 0.24368 0.331 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6217
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.621714208828905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0415 -0.7324 0.0316 0.9160 3.7004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01330 0.08710 114.959 < 2e-16 ***
## category_code_LT01_4_count 0.84347 0.08825 9.557 < 2e-16 ***
## category_code_LT01_5_count 0.92812 0.06228 14.902 < 2e-16 ***
## category_code_LT01_11_count 0.47354 0.11397 4.155 3.84e-05 ***
## category_code_LT01_12_count -0.04026 0.21296 -0.189 0.850
## category_code_LT01_14_count 0.11231 0.32837 0.342 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6217
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.621642568572665
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0417 -0.7325 0.0296 0.9160 3.7024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01199 0.08703 115.039 < 2e-16 ***
## category_code_LT01_4_count 0.84722 0.08743 9.690 < 2e-16 ***
## category_code_LT01_5_count 0.93029 0.06201 15.002 < 2e-16 ***
## category_code_LT01_11_count 0.47252 0.11428 4.135 4.18e-05 ***
## category_code_LT01_12_count -0.03402 0.21255 -0.160 0.873
## category_code_LT01_15_count 0.11582 0.75075 0.154 0.877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6216
## F-statistic: 164.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.622266649646136
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0404 -0.7312 0.0326 0.9149 3.7046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01195 0.08696 115.134 < 2e-16 ***
## category_code_LT01_4_count 0.84637 0.08695 9.734 < 2e-16 ***
## category_code_LT01_5_count 0.92851 0.06198 14.981 < 2e-16 ***
## category_code_LT01_11_count 0.46939 0.11398 4.118 4.48e-05 ***
## category_code_LT01_12_count -0.03312 0.21227 -0.156 0.876
## category_code_LT01_16_count 1.06368 1.16285 0.915 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6223
## F-statistic: 164.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.62177026147877
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0399 -0.7310 0.0346 0.9145 3.7065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01273 0.08707 114.992 < 2e-16 ***
## category_code_LT01_4_count 0.84028 0.08871 9.472 < 2e-16 ***
## category_code_LT01_5_count 0.92667 0.06209 14.924 < 2e-16 ***
## category_code_LT01_11_count 0.46551 0.10986 4.237 2.7e-05 ***
## category_code_LT01_13_count 0.08031 0.24364 0.330 0.742
## category_code_LT01_14_count 0.10819 0.32751 0.330 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6218
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.621711732508849
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0403 -0.7310 0.0325 0.9158 3.7080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01150 0.08701 115.067 < 2e-16 ***
## category_code_LT01_4_count 0.84356 0.08792 9.595 < 2e-16 ***
## category_code_LT01_5_count 0.92893 0.06178 15.036 < 2e-16 ***
## category_code_LT01_11_count 0.46509 0.11010 4.224 2.86e-05 ***
## category_code_LT01_13_count 0.08302 0.24419 0.340 0.734
## category_code_LT01_15_count 0.13658 0.75194 0.182 0.856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6217
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.622348484503359
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0390 -0.7300 0.0346 0.9118 3.7101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01146 0.08693 115.164 < 2e-16 ***
## category_code_LT01_4_count 0.84273 0.08740 9.642 < 2e-16 ***
## category_code_LT01_5_count 0.92709 0.06175 15.014 < 2e-16 ***
## category_code_LT01_11_count 0.46209 0.10984 4.207 3.07e-05 ***
## category_code_LT01_13_count 0.08816 0.24361 0.362 0.718
## category_code_LT01_16_count 1.08053 1.16341 0.929 0.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6223
## F-statistic: 164.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.621705514902868
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0405 -0.7315 0.0329 0.9195 3.7056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01288 0.08708 114.986 < 2e-16 ***
## category_code_LT01_4_count 0.84195 0.08866 9.497 < 2e-16 ***
## category_code_LT01_5_count 0.92734 0.06209 14.936 < 2e-16 ***
## category_code_LT01_11_count 0.46654 0.10991 4.245 2.62e-05 ***
## category_code_LT01_14_count 0.10739 0.32755 0.328 0.743
## category_code_LT01_15_count 0.11728 0.75034 0.156 0.876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 492 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6217
## F-statistic: 164.4 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.622356006890751
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0392 -0.7306 0.0388 0.9210 3.7077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01301 0.08700 115.086 < 2e-16 ***
## category_code_LT01_4_count 0.84036 0.08823 9.525 < 2e-16 ***
## category_code_LT01_5_count 0.92524 0.06206 14.909 < 2e-16 ***
## category_code_LT01_11_count 0.46332 0.10968 4.224 2.86e-05 ***
## category_code_LT01_14_count 0.12293 0.32765 0.375 0.708
## category_code_LT01_16_count 1.08698 1.16406 0.934 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6224
## F-statistic: 164.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.622274783734297
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0396 -0.7306 0.0336 0.9191 3.7092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01164 0.08694 115.157 < 2e-16 ***
## category_code_LT01_4_count 0.84441 0.08736 9.665 < 2e-16 ***
## category_code_LT01_5_count 0.92783 0.06174 15.027 < 2e-16 ***
## category_code_LT01_11_count 0.46313 0.10989 4.214 2.98e-05 ***
## category_code_LT01_15_count 0.14022 0.75007 0.187 0.852
## category_code_LT01_16_count 1.07196 1.16329 0.921 0.357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 492 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6223
## F-statistic: 164.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.608677454564014
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0446 -0.7902 -0.0093 0.9252 4.0302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01163 0.08859 113.008 <2e-16 ***
## category_code_LT01_4_count 1.06989 0.07037 15.205 <2e-16 ***
## category_code_LT01_5_count 0.93506 0.06334 14.763 <2e-16 ***
## category_code_LT01_12_count 0.19706 0.20852 0.945 0.345
## category_code_LT01_13_count 0.13505 0.24749 0.546 0.586
## category_code_LT01_14_count 0.12215 0.33397 0.366 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6126, Adjusted R-squared: 0.6087
## F-statistic: 155.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.608756424004072
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0449 -0.7892 0.0145 0.9317 4.0317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01014 0.08850 113.107 <2e-16 ***
## category_code_LT01_4_count 1.06894 0.06998 15.275 <2e-16 ***
## category_code_LT01_5_count 0.93758 0.06304 14.872 <2e-16 ***
## category_code_LT01_12_count 0.20395 0.20791 0.981 0.327
## category_code_LT01_13_count 0.14211 0.24793 0.573 0.567
## category_code_LT01_15_count 0.36831 0.76285 0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6127, Adjusted R-squared: 0.6088
## F-statistic: 155.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609515308699081
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0432 -0.7997 0.0115 0.9337 4.0317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01015 0.08842 113.217 <2e-16 ***
## category_code_LT01_4_count 1.06985 0.06875 15.562 <2e-16 ***
## category_code_LT01_5_count 0.93514 0.06301 14.841 <2e-16 ***
## category_code_LT01_12_count 0.20227 0.20770 0.974 0.331
## category_code_LT01_13_count 0.14370 0.24736 0.581 0.562
## category_code_LT01_16_count 1.28916 1.18191 1.091 0.276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6095
## F-statistic: 156.2 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.608597273204575
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0457 -0.7916 -0.0140 0.9245 4.0301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01181 0.08860 113.000 <2e-16 ***
## category_code_LT01_4_count 1.07092 0.07046 15.198 <2e-16 ***
## category_code_LT01_5_count 0.93626 0.06333 14.784 <2e-16 ***
## category_code_LT01_12_count 0.20171 0.20847 0.968 0.334
## category_code_LT01_14_count 0.11966 0.33403 0.358 0.720
## category_code_LT01_15_count 0.33799 0.76163 0.444 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6125, Adjusted R-squared: 0.6086
## F-statistic: 155.6 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609385111603992
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0440 -0.8016 -0.0219 0.9285 4.0298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01204 0.08851 113.117 <2e-16 ***
## category_code_LT01_4_count 1.07048 0.06945 15.414 <2e-16 ***
## category_code_LT01_5_count 0.93350 0.06330 14.747 <2e-16 ***
## category_code_LT01_12_count 0.19925 0.20824 0.957 0.339
## category_code_LT01_14_count 0.13910 0.33406 0.416 0.677
## category_code_LT01_16_count 1.29013 1.18283 1.091 0.276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6133, Adjusted R-squared: 0.6094
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609428642453154
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0443 -0.7952 0.0090 0.9416 4.0315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01037 0.08842 113.209 <2e-16 ***
## category_code_LT01_4_count 1.07078 0.06889 15.544 <2e-16 ***
## category_code_LT01_5_count 0.93638 0.06300 14.863 <2e-16 ***
## category_code_LT01_12_count 0.20711 0.20764 0.997 0.319
## category_code_LT01_15_count 0.36358 0.76103 0.478 0.633
## category_code_LT01_16_count 1.28153 1.18181 1.084 0.279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6094
## F-statistic: 156.1 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.608140620799419
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0506 -0.7945 0.0020 0.9255 4.0284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01351 0.08863 112.983 <2e-16 ***
## category_code_LT01_4_count 1.07956 0.06967 15.496 <2e-16 ***
## category_code_LT01_5_count 0.94093 0.06312 14.908 <2e-16 ***
## category_code_LT01_13_count 0.14918 0.24803 0.601 0.548
## category_code_LT01_14_count 0.14434 0.33326 0.433 0.665
## category_code_LT01_15_count 0.35632 0.76346 0.467 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 492 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6081
## F-statistic: 155.3 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608955033817944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0488 -0.7929 0.0028 0.9278 4.0282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01372 0.08854 113.103 <2e-16 ***
## category_code_LT01_4_count 1.07897 0.06857 15.735 <2e-16 ***
## category_code_LT01_5_count 0.93800 0.06309 14.868 <2e-16 ***
## category_code_LT01_13_count 0.15119 0.24743 0.611 0.541
## category_code_LT01_14_count 0.16399 0.33327 0.492 0.623
## category_code_LT01_16_count 1.32014 1.18412 1.115 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.609
## F-statistic: 155.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608964581855565
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0494 -0.7867 0.0035 0.9288 4.0301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01182 0.08846 113.180 <2e-16 ***
## category_code_LT01_4_count 1.08050 0.06779 15.940 <2e-16 ***
## category_code_LT01_5_count 0.94159 0.06274 15.007 <2e-16 ***
## category_code_LT01_13_count 0.15870 0.24792 0.640 0.522
## category_code_LT01_15_count 0.38461 0.76293 0.504 0.614
## category_code_LT01_16_count 1.30944 1.18323 1.107 0.269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 492 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.609
## F-statistic: 155.8 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608826326232421
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0501 -0.8006 0.0041 0.9278 4.0279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01398 0.08855 113.090 <2e-16 ***
## category_code_LT01_4_count 1.08106 0.06853 15.775 <2e-16 ***
## category_code_LT01_5_count 0.93948 0.06308 14.894 <2e-16 ***
## category_code_LT01_14_count 0.16184 0.33334 0.486 0.628
## category_code_LT01_15_count 0.35013 0.76160 0.460 0.646
## category_code_LT01_16_count 1.31044 1.18407 1.107 0.269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 492 degrees of freedom
## Multiple R-squared: 0.6128, Adjusted R-squared: 0.6088
## F-statistic: 155.7 on 5 and 492 DF, p-value: < 2.2e-16
##
## ########################################
## i: 6
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count 0.646829461246844
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9396 -0.7271 0.0331 0.8528 3.5098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92222 0.08503 116.686 < 2e-16 ***
## category_code_LT01_1_count 0.22549 0.08665 2.602 0.00954 **
## category_code_LT01_2_count 0.45094 0.09092 4.959 9.76e-07 ***
## category_code_LT01_3_count 0.19254 0.11246 1.712 0.08752 .
## category_code_LT01_4_count 0.50011 0.10215 4.896 1.33e-06 ***
## category_code_LT01_5_count 0.91254 0.06038 15.114 < 2e-16 ***
## category_code_LT01_6_count 0.26449 0.14952 1.769 0.07753 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.338 on 491 degrees of freedom
## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6468
## F-statistic: 152.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count 0.648760100682066
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9517 -0.7219 0.0118 0.8344 3.4965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93233 0.08473 117.220 < 2e-16 ***
## category_code_LT01_1_count 0.21781 0.08653 2.517 0.0122 *
## category_code_LT01_2_count 0.45763 0.08933 5.123 4.33e-07 ***
## category_code_LT01_3_count 0.20069 0.11196 1.792 0.0737 .
## category_code_LT01_4_count 0.47781 0.10277 4.649 4.28e-06 ***
## category_code_LT01_5_count 0.91543 0.05998 15.263 < 2e-16 ***
## category_code_LT01_7_count 0.36245 0.14992 2.418 0.0160 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.335 on 491 degrees of freedom
## Multiple R-squared: 0.653, Adjusted R-squared: 0.6488
## F-statistic: 154 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count 0.644827490525434
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9591 -0.7259 0.0495 0.8624 3.4836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93019 0.08523 116.516 < 2e-16 ***
## category_code_LT01_1_count 0.23617 0.08678 2.722 0.00673 **
## category_code_LT01_2_count 0.48445 0.08911 5.437 8.57e-08 ***
## category_code_LT01_3_count 0.20573 0.11260 1.827 0.06829 .
## category_code_LT01_4_count 0.52660 0.10135 5.196 2.99e-07 ***
## category_code_LT01_5_count 0.92914 0.06091 15.254 < 2e-16 ***
## category_code_LT01_8_count -0.15588 0.26584 -0.586 0.55789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6491, Adjusted R-squared: 0.6448
## F-statistic: 151.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count 0.645764490043902
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9484 -0.7164 0.0956 0.8681 3.4987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92686 0.08509 116.658 < 2e-16 ***
## category_code_LT01_1_count 0.23134 0.08666 2.670 0.00785 **
## category_code_LT01_2_count 0.47301 0.08948 5.286 1.88e-07 ***
## category_code_LT01_3_count 0.18593 0.11335 1.640 0.10158
## category_code_LT01_4_count 0.52317 0.10125 5.167 3.46e-07 ***
## category_code_LT01_5_count 0.91844 0.06027 15.238 < 2e-16 ***
## category_code_LT01_9_count 0.28279 0.22059 1.282 0.20045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.34 on 491 degrees of freedom
## Multiple R-squared: 0.65, Adjusted R-squared: 0.6458
## F-statistic: 152 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count 0.644962118926615
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9370 -0.7261 0.0549 0.8712 3.4194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91181 0.08831 112.235 < 2e-16 ***
## category_code_LT01_1_count 0.23722 0.08679 2.733 0.0065 **
## category_code_LT01_2_count 0.48057 0.08930 5.381 1.15e-07 ***
## category_code_LT01_3_count 0.19026 0.11423 1.666 0.0964 .
## category_code_LT01_4_count 0.52464 0.10136 5.176 3.31e-07 ***
## category_code_LT01_5_count 0.92383 0.06020 15.346 < 2e-16 ***
## category_code_LT01_10_count 0.08047 0.11052 0.728 0.4669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6492, Adjusted R-squared: 0.645
## F-statistic: 151.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count 0.645846607718405
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9594 -0.7248 0.0300 0.8593 3.4866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93604 0.08525 116.558 < 2e-16 ***
## category_code_LT01_1_count 0.21807 0.08751 2.492 0.013 *
## category_code_LT01_2_count 0.44142 0.09488 4.653 4.22e-06 ***
## category_code_LT01_3_count 0.17770 0.11421 1.556 0.120
## category_code_LT01_4_count 0.49460 0.10400 4.756 2.60e-06 ***
## category_code_LT01_5_count 0.92111 0.06016 15.311 < 2e-16 ***
## category_code_LT01_11_count 0.15752 0.11881 1.326 0.186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.34 on 491 degrees of freedom
## Multiple R-squared: 0.6501, Adjusted R-squared: 0.6458
## F-statistic: 152.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count 0.64465102316476
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9551 -0.7261 0.0550 0.8652 3.4891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92861 0.08522 116.511 < 2e-16 ***
## category_code_LT01_1_count 0.23769 0.08729 2.723 0.0067 **
## category_code_LT01_2_count 0.48863 0.08982 5.440 8.43e-08 ***
## category_code_LT01_3_count 0.20583 0.11269 1.826 0.0684 .
## category_code_LT01_4_count 0.52719 0.10141 5.199 2.95e-07 ***
## category_code_LT01_5_count 0.92560 0.06051 15.296 < 2e-16 ***
## category_code_LT01_12_count -0.06376 0.20179 -0.316 0.7522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6489, Adjusted R-squared: 0.6447
## F-statistic: 151.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count 0.644585289579423
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9540 -0.7189 0.0617 0.8657 3.4908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92870 0.08523 116.496 < 2e-16 ***
## category_code_LT01_1_count 0.23576 0.08757 2.692 0.00734 **
## category_code_LT01_2_count 0.48539 0.08919 5.442 8.31e-08 ***
## category_code_LT01_3_count 0.20435 0.11262 1.815 0.07020 .
## category_code_LT01_4_count 0.52676 0.10147 5.191 3.07e-07 ***
## category_code_LT01_5_count 0.92394 0.06027 15.330 < 2e-16 ***
## category_code_LT01_13_count -0.02262 0.23825 -0.095 0.92441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6489, Adjusted R-squared: 0.6446
## F-statistic: 151.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count 0.644580066927905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9540 -0.7182 0.0532 0.8657 3.4908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92860 0.08535 116.332 < 2e-16 ***
## category_code_LT01_1_count 0.23501 0.08723 2.694 0.0073 **
## category_code_LT01_2_count 0.48531 0.08927 5.436 8.59e-08 ***
## category_code_LT01_3_count 0.20411 0.11286 1.809 0.0711 .
## category_code_LT01_4_count 0.52683 0.10200 5.165 3.50e-07 ***
## category_code_LT01_5_count 0.92403 0.06062 15.244 < 2e-16 ***
## category_code_LT01_14_count -0.01356 0.31999 -0.042 0.9662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6489, Adjusted R-squared: 0.6446
## F-statistic: 151.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count 0.6448986429675
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9521 -0.7169 0.0613 0.8673 3.4930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92733 0.08521 116.501 < 2e-16 ***
## category_code_LT01_1_count 0.24397 0.08786 2.777 0.0057 **
## category_code_LT01_2_count 0.48656 0.08912 5.460 7.59e-08 ***
## category_code_LT01_3_count 0.21208 0.11315 1.874 0.0615 .
## category_code_LT01_4_count 0.52540 0.10135 5.184 3.18e-07 ***
## category_code_LT01_5_count 0.92318 0.06021 15.332 < 2e-16 ***
## category_code_LT01_15_count -0.49329 0.74174 -0.665 0.5063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6492, Adjusted R-squared: 0.6449
## F-statistic: 151.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_16_count 0.644627458196133
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9541 -0.7186 0.0589 0.8654 3.4909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92906 0.08522 116.507 < 2e-16 ***
## category_code_LT01_1_count 0.23551 0.08683 2.712 0.00691 **
## category_code_LT01_2_count 0.48286 0.08954 5.393 1.08e-07 ***
## category_code_LT01_3_count 0.20134 0.11324 1.778 0.07601 .
## category_code_LT01_4_count 0.52773 0.10152 5.198 2.95e-07 ***
## category_code_LT01_5_count 0.92354 0.06023 15.333 < 2e-16 ***
## category_code_LT01_16_count 0.29613 1.14172 0.259 0.79546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6489, Adjusted R-squared: 0.6446
## F-statistic: 151.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.637719986279373
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9636 -0.7468 0.0516 0.8345 3.4752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92817 0.08613 115.266 < 2e-16 ***
## category_code_LT01_1_count 0.31666 0.08429 3.757 0.000193 ***
## category_code_LT01_2_count 0.59295 0.08377 7.078 5.08e-12 ***
## category_code_LT01_3_count 0.32076 0.10961 2.926 0.003587 **
## category_code_LT01_5_count 0.93858 0.06071 15.460 < 2e-16 ***
## category_code_LT01_6_count 0.36688 0.14983 2.449 0.014690 *
## category_code_LT01_7_count 0.49576 0.14933 3.320 0.000967 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.356 on 491 degrees of freedom
## Multiple R-squared: 0.6421, Adjusted R-squared: 0.6377
## F-statistic: 146.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.629903900635273
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9771 -0.7923 0.0272 0.8432 3.4679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92529 0.08708 113.984 < 2e-16 ***
## category_code_LT01_1_count 0.35946 0.08430 4.264 2.41e-05 ***
## category_code_LT01_2_count 0.65880 0.08219 8.015 8.08e-15 ***
## category_code_LT01_3_count 0.34846 0.11051 3.153 0.00171 **
## category_code_LT01_5_count 0.96221 0.06180 15.569 < 2e-16 ***
## category_code_LT01_6_count 0.37459 0.15155 2.472 0.01378 *
## category_code_LT01_8_count -0.17603 0.27157 -0.648 0.51716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.630851398956744
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9659 -0.7717 0.0705 0.8568 3.4712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92196 0.08695 114.115 < 2e-16 ***
## category_code_LT01_1_count 0.35399 0.08421 4.204 3.12e-05 ***
## category_code_LT01_2_count 0.64726 0.08266 7.831 3.01e-14 ***
## category_code_LT01_3_count 0.32754 0.11138 2.941 0.00343 **
## category_code_LT01_5_count 0.95085 0.06119 15.539 < 2e-16 ***
## category_code_LT01_6_count 0.36405 0.15133 2.406 0.01651 *
## category_code_LT01_9_count 0.29209 0.22525 1.297 0.19534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.629761582154813
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9606 -0.8060 0.0523 0.8552 3.4807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91250 0.09018 109.914 < 2e-16 ***
## category_code_LT01_1_count 0.35998 0.08440 4.265 2.39e-05 ***
## category_code_LT01_2_count 0.65830 0.08227 8.002 8.88e-15 ***
## category_code_LT01_3_count 0.33798 0.11216 3.013 0.00272 **
## category_code_LT01_5_count 0.95679 0.06115 15.648 < 2e-16 ***
## category_code_LT01_6_count 0.36013 0.15308 2.352 0.01904 *
## category_code_LT01_10_count 0.05484 0.11403 0.481 0.63081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6342, Adjusted R-squared: 0.6298
## F-statistic: 141.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.633082608567077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9788 -0.7761 0.0903 0.8295 3.4570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93618 0.08686 114.393 < 2e-16 ***
## category_code_LT01_1_count 0.31903 0.08581 3.718 0.000224 ***
## category_code_LT01_2_count 0.57236 0.09131 6.268 8.01e-10 ***
## category_code_LT01_3_count 0.28992 0.11316 2.562 0.010700 *
## category_code_LT01_5_count 0.94899 0.06096 15.569 < 2e-16 ***
## category_code_LT01_6_count 0.33108 0.15190 2.180 0.029765 *
## category_code_LT01_11_count 0.25640 0.11855 2.163 0.031043 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6375, Adjusted R-squared: 0.6331
## F-statistic: 143.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.629711036147685
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9727 -0.7968 0.0355 0.8396 3.4697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92343 0.08707 113.965 < 2e-16 ***
## category_code_LT01_1_count 0.36185 0.08485 4.264 2.41e-05 ***
## category_code_LT01_2_count 0.66422 0.08283 8.019 7.85e-15 ***
## category_code_LT01_3_count 0.34896 0.11060 3.155 0.0017 **
## category_code_LT01_5_count 0.95855 0.06139 15.613 < 2e-16 ***
## category_code_LT01_6_count 0.37649 0.15212 2.475 0.0137 *
## category_code_LT01_12_count -0.08380 0.20680 -0.405 0.6855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6342, Adjusted R-squared: 0.6297
## F-statistic: 141.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.629611739113646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9714 -0.7910 0.0474 0.8425 3.4692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92398 0.08709 113.957 < 2e-16 ***
## category_code_LT01_1_count 0.35544 0.08538 4.163 3.71e-05 ***
## category_code_LT01_2_count 0.65902 0.08240 7.997 9.20e-15 ***
## category_code_LT01_3_count 0.34703 0.11054 3.139 0.00179 **
## category_code_LT01_5_count 0.95580 0.06121 15.615 < 2e-16 ***
## category_code_LT01_6_count 0.37146 0.15154 2.451 0.01458 *
## category_code_LT01_13_count 0.04384 0.24307 0.180 0.85695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6341, Adjusted R-squared: 0.6296
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.629994603219543
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9702 -0.7941 0.0541 0.8488 3.4662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92694 0.08714 113.920 < 2e-16 ***
## category_code_LT01_1_count 0.34819 0.08528 4.083 5.20e-05 ***
## category_code_LT01_2_count 0.65078 0.08312 7.829 3.05e-14 ***
## category_code_LT01_3_count 0.34925 0.11051 3.160 0.00167 **
## category_code_LT01_5_count 0.94985 0.06174 15.385 < 2e-16 ***
## category_code_LT01_6_count 0.38071 0.15202 2.504 0.01259 *
## category_code_LT01_14_count 0.23956 0.32581 0.735 0.46253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.630040121915482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9691 -0.7887 0.0522 0.8448 3.4712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92200 0.08706 113.965 < 2e-16 ***
## category_code_LT01_1_count 0.36853 0.08536 4.317 1.91e-05 ***
## category_code_LT01_2_count 0.66086 0.08216 8.044 6.59e-15 ***
## category_code_LT01_3_count 0.35574 0.11102 3.204 0.00144 **
## category_code_LT01_5_count 0.95538 0.06112 15.630 < 2e-16 ***
## category_code_LT01_6_count 0.37328 0.15144 2.465 0.01405 *
## category_code_LT01_15_count -0.58705 0.75719 -0.775 0.43853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.629632695561587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9715 -0.7911 0.0465 0.8454 3.4692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92398 0.08708 113.963 < 2e-16 ***
## category_code_LT01_1_count 0.35886 0.08439 4.252 2.53e-05 ***
## category_code_LT01_2_count 0.65782 0.08270 7.954 1.25e-14 ***
## category_code_LT01_3_count 0.34433 0.11114 3.098 0.00206 **
## category_code_LT01_5_count 0.95603 0.06116 15.632 < 2e-16 ***
## category_code_LT01_6_count 0.37471 0.15232 2.460 0.01424 *
## category_code_LT01_16_count 0.28742 1.17036 0.246 0.80611
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6341, Adjusted R-squared: 0.6296
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.633619154420195
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9921 -0.7439 0.0316 0.7972 3.4539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93924 0.08657 114.817 < 2e-16 ***
## category_code_LT01_1_count 0.34080 0.08429 4.043 6.12e-05 ***
## category_code_LT01_2_count 0.65549 0.08011 8.182 2.41e-15 ***
## category_code_LT01_3_count 0.35036 0.10964 3.196 0.001485 **
## category_code_LT01_5_count 0.96367 0.06125 15.733 < 2e-16 ***
## category_code_LT01_7_count 0.50166 0.15023 3.339 0.000904 ***
## category_code_LT01_8_count -0.17772 0.27013 -0.658 0.510905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 491 degrees of freedom
## Multiple R-squared: 0.638, Adjusted R-squared: 0.6336
## F-statistic: 144.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.634180928049549
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9814 -0.7356 0.0471 0.7561 3.4574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93580 0.08648 114.890 < 2e-16 ***
## category_code_LT01_1_count 0.33685 0.08422 3.999 7.33e-05 ***
## category_code_LT01_2_count 0.64681 0.08053 8.032 7.18e-15 ***
## category_code_LT01_3_count 0.33281 0.11053 3.011 0.00274 **
## category_code_LT01_5_count 0.95327 0.06064 15.721 < 2e-16 ***
## category_code_LT01_7_count 0.48375 0.15067 3.211 0.00141 **
## category_code_LT01_9_count 0.24522 0.22503 1.090 0.27637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.362 on 491 degrees of freedom
## Multiple R-squared: 0.6386, Adjusted R-squared: 0.6342
## F-statistic: 144.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.633547699039975
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9725 -0.7724 0.0453 0.8210 3.4694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92376 0.08978 110.540 < 2e-16 ***
## category_code_LT01_1_count 0.34150 0.08436 4.048 6.00e-05 ***
## category_code_LT01_2_count 0.65312 0.08032 8.132 3.48e-15 ***
## category_code_LT01_3_count 0.33735 0.11143 3.027 0.00260 **
## category_code_LT01_5_count 0.95777 0.06056 15.816 < 2e-16 ***
## category_code_LT01_7_count 0.49189 0.15063 3.266 0.00117 **
## category_code_LT01_10_count 0.06536 0.11258 0.581 0.56182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 491 degrees of freedom
## Multiple R-squared: 0.638, Adjusted R-squared: 0.6335
## F-statistic: 144.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.635383725912097
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9912 -0.7672 0.0435 0.8205 3.4471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94605 0.08647 115.024 < 2e-16 ***
## category_code_LT01_1_count 0.31189 0.08562 3.643 0.000299 ***
## category_code_LT01_2_count 0.59038 0.08911 6.625 9.12e-11 ***
## category_code_LT01_3_count 0.30463 0.11250 2.708 0.007007 **
## category_code_LT01_5_count 0.95251 0.06048 15.749 < 2e-16 ***
## category_code_LT01_7_count 0.43414 0.15466 2.807 0.005200 **
## category_code_LT01_11_count 0.20307 0.12112 1.677 0.094248 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.36 on 491 degrees of freedom
## Multiple R-squared: 0.6398, Adjusted R-squared: 0.6354
## F-statistic: 145.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.633313414426661
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9868 -0.7330 0.0274 0.8053 3.4556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93753 0.08657 114.792 < 2e-16 ***
## category_code_LT01_1_count 0.34084 0.08497 4.012 6.97e-05 ***
## category_code_LT01_2_count 0.65845 0.08114 8.115 3.93e-15 ***
## category_code_LT01_3_count 0.34977 0.10980 3.186 0.001536 **
## category_code_LT01_5_count 0.95855 0.06090 15.739 < 2e-16 ***
## category_code_LT01_7_count 0.49846 0.15023 3.318 0.000974 ***
## category_code_LT01_12_count -0.03114 0.20492 -0.152 0.879281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6377, Adjusted R-squared: 0.6333
## F-statistic: 144.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.633340868293599
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9864 -0.7352 0.0333 0.8066 3.4557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93742 0.08657 114.792 < 2e-16 ***
## category_code_LT01_1_count 0.34215 0.08513 4.019 6.76e-05 ***
## category_code_LT01_2_count 0.65721 0.08018 8.197 2.17e-15 ***
## category_code_LT01_3_count 0.34888 0.10966 3.182 0.001557 **
## category_code_LT01_5_count 0.95807 0.06061 15.808 < 2e-16 ***
## category_code_LT01_7_count 0.50272 0.15114 3.326 0.000946 ***
## category_code_LT01_13_count -0.05951 0.24325 -0.245 0.806823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6378, Adjusted R-squared: 0.6333
## F-statistic: 144.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.633347773728052
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7576 0.0356 0.8083 3.4544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93880 0.08668 114.662 < 2e-16 ***
## category_code_LT01_1_count 0.33625 0.08505 3.954 8.83e-05 ***
## category_code_LT01_2_count 0.65424 0.08059 8.118 3.85e-15 ***
## category_code_LT01_3_count 0.35011 0.10974 3.190 0.00151 **
## category_code_LT01_5_count 0.95560 0.06105 15.654 < 2e-16 ***
## category_code_LT01_7_count 0.49559 0.15067 3.289 0.00108 **
## category_code_LT01_14_count 0.08518 0.32402 0.263 0.79275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6378, Adjusted R-squared: 0.6333
## F-statistic: 144.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.633522536054975
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9847 -0.7419 0.0371 0.8013 3.4568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93635 0.08657 114.773 < 2e-16 ***
## category_code_LT01_1_count 0.34724 0.08552 4.060 5.70e-05 ***
## category_code_LT01_2_count 0.65798 0.08015 8.209 1.98e-15 ***
## category_code_LT01_3_count 0.35537 0.11025 3.223 0.00135 **
## category_code_LT01_5_count 0.95720 0.06056 15.805 < 2e-16 ***
## category_code_LT01_7_count 0.49423 0.15040 3.286 0.00109 **
## category_code_LT01_15_count -0.41552 0.75452 -0.551 0.58208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 491 degrees of freedom
## Multiple R-squared: 0.6379, Adjusted R-squared: 0.6335
## F-statistic: 144.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.633299313054336
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7398 0.0364 0.8075 3.4555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93770 0.08658 114.785 < 2e-16 ***
## category_code_LT01_1_count 0.33952 0.08442 4.022 6.69e-05 ***
## category_code_LT01_2_count 0.65607 0.08042 8.158 2.87e-15 ***
## category_code_LT01_3_count 0.34826 0.11016 3.161 0.001668 **
## category_code_LT01_5_count 0.95758 0.06058 15.807 < 2e-16 ***
## category_code_LT01_7_count 0.49887 0.15027 3.320 0.000968 ***
## category_code_LT01_16_count 0.07517 1.15850 0.065 0.948294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6377, Adjusted R-squared: 0.6333
## F-statistic: 144.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.626769074424441
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9933 -0.7764 0.0531 0.8062 3.4605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93261 0.08737 113.680 < 2e-16 ***
## category_code_LT01_1_count 0.37795 0.08420 4.489 8.94e-06 ***
## category_code_LT01_2_count 0.70907 0.07890 8.987 < 2e-16 ***
## category_code_LT01_3_count 0.35552 0.11145 3.190 0.00151 **
## category_code_LT01_5_count 0.97501 0.06174 15.791 < 2e-16 ***
## category_code_LT01_8_count -0.16206 0.27265 -0.594 0.55251
## category_code_LT01_9_count 0.31496 0.22647 1.391 0.16493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.625834650275205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9794 -0.7527 0.0200 0.8177 3.4785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91463 0.09068 109.336 < 2e-16 ***
## category_code_LT01_1_count 0.38502 0.08430 4.567 6.26e-06 ***
## category_code_LT01_2_count 0.71789 0.07860 9.133 < 2e-16 ***
## category_code_LT01_3_count 0.35981 0.11242 3.201 0.00146 **
## category_code_LT01_5_count 0.98092 0.06168 15.904 < 2e-16 ***
## category_code_LT01_8_count -0.15511 0.27292 -0.568 0.57006
## category_code_LT01_10_count 0.09513 0.11345 0.839 0.40213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.629711379784417
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0042 -0.7668 0.0631 0.8315 3.4460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94718 0.08717 114.114 < 2e-16 ***
## category_code_LT01_1_count 0.33619 0.08597 3.911 0.000105 ***
## category_code_LT01_2_count 0.61867 0.08923 6.934 1.3e-11 ***
## category_code_LT01_3_count 0.30969 0.11341 2.731 0.006546 **
## category_code_LT01_5_count 0.96976 0.06153 15.761 < 2e-16 ***
## category_code_LT01_8_count -0.13219 0.27154 -0.487 0.626595
## category_code_LT01_11_count 0.28607 0.11826 2.419 0.015930 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6342, Adjusted R-squared: 0.6297
## F-statistic: 141.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.625317912691046
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0000 -0.7524 0.0468 0.8288 3.4586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93458 0.08753 113.495 < 2e-16 ***
## category_code_LT01_1_count 0.38415 0.08497 4.521 7.71e-06 ***
## category_code_LT01_2_count 0.72620 0.07933 9.154 < 2e-16 ***
## category_code_LT01_3_count 0.37802 0.11070 3.415 0.000691 ***
## category_code_LT01_5_count 0.98179 0.06201 15.832 < 2e-16 ***
## category_code_LT01_8_count -0.14872 0.27320 -0.544 0.586439
## category_code_LT01_12_count -0.03279 0.20725 -0.158 0.874343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.625304625257297
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9993 -0.7546 0.0484 0.8296 3.4584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93476 0.08754 113.493 < 2e-16 ***
## category_code_LT01_1_count 0.38129 0.08540 4.465 9.95e-06 ***
## category_code_LT01_2_count 0.72374 0.07847 9.223 < 2e-16 ***
## category_code_LT01_3_count 0.37710 0.11058 3.410 0.000702 ***
## category_code_LT01_5_count 0.98057 0.06180 15.866 < 2e-16 ***
## category_code_LT01_8_count -0.14870 0.27358 -0.544 0.587012
## category_code_LT01_13_count 0.02138 0.24488 0.087 0.930460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.62550487780541
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9990 -0.7523 0.0443 0.8217 3.4561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93710 0.08763 113.396 < 2e-16 ***
## category_code_LT01_1_count 0.37606 0.08518 4.415 1.24e-05 ***
## category_code_LT01_2_count 0.71882 0.07896 9.104 < 2e-16 ***
## category_code_LT01_3_count 0.37919 0.11061 3.428 0.000659 ***
## category_code_LT01_5_count 0.97672 0.06221 15.699 < 2e-16 ***
## category_code_LT01_8_count -0.15211 0.27300 -0.557 0.577667
## category_code_LT01_14_count 0.16972 0.32652 0.520 0.603447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.62569084222635
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9973 -0.7606 0.0547 0.8291 3.4601
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93305 0.08752 113.499 < 2e-16 ***
## category_code_LT01_1_count 0.39251 0.08542 4.595 5.5e-06 ***
## category_code_LT01_2_count 0.72533 0.07828 9.266 < 2e-16 ***
## category_code_LT01_3_count 0.38529 0.11110 3.468 0.00057 ***
## category_code_LT01_5_count 0.98009 0.06170 15.885 < 2e-16 ***
## category_code_LT01_8_count -0.14936 0.27291 -0.547 0.58442
## category_code_LT01_15_count -0.54605 0.76146 -0.717 0.47365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.625299012422337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9995 -0.7550 0.0481 0.8294 3.4584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93471 0.08754 113.486 < 2e-16 ***
## category_code_LT01_1_count 0.38256 0.08446 4.529 7.43e-06 ***
## category_code_LT01_2_count 0.72408 0.07856 9.217 < 2e-16 ***
## category_code_LT01_3_count 0.37700 0.11106 3.395 0.000743 ***
## category_code_LT01_5_count 0.98085 0.06172 15.891 < 2e-16 ***
## category_code_LT01_8_count -0.15041 0.27339 -0.550 0.582466
## category_code_LT01_16_count 0.01919 1.17223 0.016 0.986944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.626867362174898
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9716 -0.7814 0.0658 0.8048 3.4786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91459 0.09054 109.507 < 2e-16 ***
## category_code_LT01_1_count 0.37865 0.08421 4.496 8.64e-06 ***
## category_code_LT01_2_count 0.70536 0.07914 8.913 < 2e-16 ***
## category_code_LT01_3_count 0.34098 0.11307 3.016 0.0027 **
## category_code_LT01_5_count 0.96968 0.06103 15.889 < 2e-16 ***
## category_code_LT01_9_count 0.29506 0.22745 1.297 0.1952
## category_code_LT01_10_count 0.07909 0.11383 0.695 0.4875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.630825900397986
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9937 -0.7334 0.0692 0.8479 3.4493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94383 0.08702 114.265 < 2e-16 ***
## category_code_LT01_1_count 0.33092 0.08582 3.856 0.000131 ***
## category_code_LT01_2_count 0.60603 0.08960 6.764 3.84e-11 ***
## category_code_LT01_3_count 0.28878 0.11415 2.530 0.011724 *
## category_code_LT01_5_count 0.95940 0.06085 15.768 < 2e-16 ***
## category_code_LT01_9_count 0.29537 0.22521 1.311 0.190302
## category_code_LT01_11_count 0.28324 0.11809 2.398 0.016835 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6308
## F-statistic: 142.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.626522106820004
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9887 -0.7782 0.0693 0.8078 3.4621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93109 0.08737 113.665 < 2e-16 ***
## category_code_LT01_1_count 0.37814 0.08486 4.456 1.04e-05 ***
## category_code_LT01_2_count 0.71198 0.07994 8.906 < 2e-16 ***
## category_code_LT01_3_count 0.35531 0.11161 3.184 0.00155 **
## category_code_LT01_5_count 0.97052 0.06138 15.811 < 2e-16 ***
## category_code_LT01_9_count 0.31051 0.22644 1.371 0.17091
## category_code_LT01_12_count -0.03486 0.20680 -0.169 0.86623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.626536618505105
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9878 -0.7781 0.0640 0.8114 3.4618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93139 0.08737 113.667 < 2e-16 ***
## category_code_LT01_1_count 0.37337 0.08529 4.378 1.47e-05 ***
## category_code_LT01_2_count 0.70853 0.07914 8.953 < 2e-16 ***
## category_code_LT01_3_count 0.35400 0.11148 3.175 0.00159 **
## category_code_LT01_5_count 0.96883 0.06112 15.851 < 2e-16 ***
## category_code_LT01_9_count 0.31420 0.22698 1.384 0.16691
## category_code_LT01_13_count 0.05330 0.24460 0.218 0.82759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.626633788052392
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9878 -0.7791 0.0649 0.8100 3.4600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93317 0.08748 113.542 < 2e-16 ***
## category_code_LT01_1_count 0.37125 0.08505 4.365 1.55e-05 ***
## category_code_LT01_2_count 0.70582 0.07948 8.881 < 2e-16 ***
## category_code_LT01_3_count 0.35644 0.11156 3.195 0.00149 **
## category_code_LT01_5_count 0.96621 0.06154 15.701 < 2e-16 ***
## category_code_LT01_9_count 0.30414 0.22695 1.340 0.18082
## category_code_LT01_14_count 0.13682 0.32679 0.419 0.67564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6266
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.626828562201142
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7764 0.0761 0.8095 3.4634
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92975 0.08736 113.662 < 2e-16 ***
## category_code_LT01_1_count 0.38564 0.08534 4.519 7.79e-06 ***
## category_code_LT01_2_count 0.71121 0.07891 9.013 < 2e-16 ***
## category_code_LT01_3_count 0.36230 0.11208 3.233 0.00131 **
## category_code_LT01_5_count 0.96892 0.06103 15.875 < 2e-16 ***
## category_code_LT01_9_count 0.30378 0.22659 1.341 0.18065
## category_code_LT01_15_count -0.50006 0.76113 -0.657 0.51149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.626501443025592
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9880 -0.7780 0.0642 0.8094 3.4620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93115 0.08738 113.655 < 2e-16 ***
## category_code_LT01_1_count 0.37617 0.08434 4.460 1.02e-05 ***
## category_code_LT01_2_count 0.71004 0.07916 8.970 < 2e-16 ***
## category_code_LT01_3_count 0.35473 0.11194 3.169 0.00162 **
## category_code_LT01_5_count 0.96946 0.06106 15.878 < 2e-16 ***
## category_code_LT01_9_count 0.31088 0.22647 1.373 0.17047
## category_code_LT01_16_count -0.04118 1.16906 -0.035 0.97192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.630023249488358
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9806 -0.7724 0.0706 0.8270 3.4664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92680 0.09032 109.911 < 2e-16 ***
## category_code_LT01_1_count 0.33711 0.08592 3.923 9.98e-05 ***
## category_code_LT01_2_count 0.61289 0.08946 6.851 2.21e-11 ***
## category_code_LT01_3_count 0.29171 0.11513 2.534 0.0116 *
## category_code_LT01_5_count 0.96505 0.06076 15.884 < 2e-16 ***
## category_code_LT01_10_count 0.09101 0.11279 0.807 0.4201
## category_code_LT01_11_count 0.28670 0.11818 2.426 0.0156 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.625620398701883
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9752 -0.7628 0.0241 0.8191 3.4799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91326 0.09070 109.301 < 2e-16 ***
## category_code_LT01_1_count 0.38559 0.08496 4.538 7.14e-06 ***
## category_code_LT01_2_count 0.72107 0.07960 9.058 < 2e-16 ***
## category_code_LT01_3_count 0.35966 0.11254 3.196 0.00148 **
## category_code_LT01_5_count 0.97681 0.06129 15.938 < 2e-16 ***
## category_code_LT01_10_count 0.09452 0.11352 0.833 0.40546
## category_code_LT01_12_count -0.04238 0.20717 -0.205 0.83800
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.625595351371455
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9746 -0.7630 0.0276 0.8234 3.4794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91371 0.09071 109.294 < 2e-16 ***
## category_code_LT01_1_count 0.38211 0.08535 4.477 9.43e-06 ***
## category_code_LT01_2_count 0.71805 0.07878 9.115 < 2e-16 ***
## category_code_LT01_3_count 0.35869 0.11244 3.190 0.00151 **
## category_code_LT01_5_count 0.97528 0.06102 15.984 < 2e-16 ***
## category_code_LT01_10_count 0.09340 0.11352 0.823 0.41105
## category_code_LT01_13_count 0.02318 0.24444 0.095 0.92447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.625672295564193
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9762 -0.7604 0.0399 0.8340 3.4762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91699 0.09127 108.651 < 2e-16 ***
## category_code_LT01_1_count 0.37894 0.08533 4.441 1.11e-05 ***
## category_code_LT01_2_count 0.71559 0.07911 9.046 < 2e-16 ***
## category_code_LT01_3_count 0.36162 0.11278 3.207 0.00143 **
## category_code_LT01_5_count 0.97279 0.06152 15.814 < 2e-16 ***
## category_code_LT01_10_count 0.08487 0.11656 0.728 0.46688
## category_code_LT01_14_count 0.11119 0.33537 0.332 0.74037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.626046836936742
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9711 -0.7598 0.0295 0.8130 3.4826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91055 0.09072 109.248 < 2e-16 ***
## category_code_LT01_1_count 0.39442 0.08542 4.617 4.97e-06 ***
## category_code_LT01_2_count 0.71937 0.07858 9.154 < 2e-16 ***
## category_code_LT01_3_count 0.36643 0.11281 3.248 0.00124 **
## category_code_LT01_5_count 0.97473 0.06094 15.996 < 2e-16 ***
## category_code_LT01_10_count 0.09954 0.11364 0.876 0.38150
## category_code_LT01_15_count -0.59171 0.76274 -0.776 0.43826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.625589042794292
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9746 -0.7636 0.0271 0.8223 3.4797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91350 0.09070 109.296 < 2e-16 ***
## category_code_LT01_1_count 0.38325 0.08442 4.540 7.09e-06 ***
## category_code_LT01_2_count 0.71868 0.07887 9.113 < 2e-16 ***
## category_code_LT01_3_count 0.35895 0.11289 3.180 0.00157 **
## category_code_LT01_5_count 0.97553 0.06097 16.001 < 2e-16 ***
## category_code_LT01_10_count 0.09380 0.11348 0.827 0.40887
## category_code_LT01_16_count -0.03148 1.17052 -0.027 0.97856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.629931144665542
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0031 -0.7545 0.0544 0.8184 3.4467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94648 0.08712 114.176 < 2e-16 ***
## category_code_LT01_1_count 0.33937 0.08613 3.940 9.32e-05 ***
## category_code_LT01_2_count 0.62086 0.08926 6.956 1.12e-11 ***
## category_code_LT01_3_count 0.30765 0.11333 2.715 0.00687 **
## category_code_LT01_5_count 0.96907 0.06101 15.884 < 2e-16 ***
## category_code_LT01_11_count 0.30689 0.12111 2.534 0.01159 *
## category_code_LT01_12_count -0.15339 0.21095 -0.727 0.46749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.629533910179342
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9999 -0.7762 0.0652 0.8210 3.4471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.946102 0.087164 114.108 < 2e-16 ***
## category_code_LT01_1_count 0.334047 0.086855 3.846 0.000136 ***
## category_code_LT01_2_count 0.618389 0.089326 6.923 1.39e-11 ***
## category_code_LT01_3_count 0.308158 0.113391 2.718 0.006807 **
## category_code_LT01_5_count 0.965008 0.060844 15.860 < 2e-16 ***
## category_code_LT01_11_count 0.287485 0.118315 2.430 0.015463 *
## category_code_LT01_13_count 0.009968 0.243160 0.041 0.967318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.634, Adjusted R-squared: 0.6295
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.629683731515363
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7584 0.0660 0.8412 3.4451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94806 0.08726 114.011 < 2e-16 ***
## category_code_LT01_1_count 0.32930 0.08671 3.798 0.000164 ***
## category_code_LT01_2_count 0.61450 0.08969 6.852 2.2e-11 ***
## category_code_LT01_3_count 0.31021 0.11346 2.734 0.006482 **
## category_code_LT01_5_count 0.96157 0.06130 15.687 < 2e-16 ***
## category_code_LT01_11_count 0.28617 0.11827 2.420 0.015898 *
## category_code_LT01_14_count 0.14537 0.32479 0.448 0.654653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6342, Adjusted R-squared: 0.6297
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.629914941833
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9978 -0.7726 0.0651 0.8230 3.4487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94444 0.08715 114.114 < 2e-16 ***
## category_code_LT01_1_count 0.34455 0.08701 3.960 8.61e-05 ***
## category_code_LT01_2_count 0.61981 0.08922 6.947 1.19e-11 ***
## category_code_LT01_3_count 0.31626 0.11390 2.777 0.0057 **
## category_code_LT01_5_count 0.96440 0.06077 15.869 < 2e-16 ***
## category_code_LT01_11_count 0.28726 0.11819 2.430 0.0154 *
## category_code_LT01_15_count -0.53923 0.75716 -0.712 0.4767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.629536129873302
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0000 -0.7726 0.0650 0.8303 3.4470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94615 0.08717 114.102 < 2e-16 ***
## category_code_LT01_1_count 0.33486 0.08603 3.892 0.000113 ***
## category_code_LT01_2_count 0.61801 0.08958 6.899 1.62e-11 ***
## category_code_LT01_3_count 0.30738 0.11395 2.697 0.007227 **
## category_code_LT01_5_count 0.96508 0.06080 15.874 < 2e-16 ***
## category_code_LT01_11_count 0.28791 0.11831 2.433 0.015310 *
## category_code_LT01_16_count 0.07919 1.16474 0.068 0.945823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.634, Adjusted R-squared: 0.6295
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.625103295079344
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7764 0.0480 0.8305 3.4598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93336 0.08753 113.486 < 2e-16 ***
## category_code_LT01_1_count 0.38118 0.08599 4.433 1.15e-05 ***
## category_code_LT01_2_count 0.72632 0.07951 9.135 < 2e-16 ***
## category_code_LT01_3_count 0.37673 0.11071 3.403 0.000722 ***
## category_code_LT01_5_count 0.97643 0.06138 15.908 < 2e-16 ***
## category_code_LT01_12_count -0.03682 0.20720 -0.178 0.859016
## category_code_LT01_13_count 0.03002 0.24448 0.123 0.902320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6251
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.625301671179254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7729 0.0412 0.8224 3.4575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93565 0.08762 113.394 < 2e-16 ***
## category_code_LT01_1_count 0.37667 0.08574 4.393 1.37e-05 ***
## category_code_LT01_2_count 0.72196 0.07989 9.037 < 2e-16 ***
## category_code_LT01_3_count 0.37901 0.11076 3.422 0.000674 ***
## category_code_LT01_5_count 0.97271 0.06179 15.741 < 2e-16 ***
## category_code_LT01_12_count -0.04353 0.20756 -0.210 0.833962
## category_code_LT01_14_count 0.17162 0.32724 0.524 0.600210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.625496736906086
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9931 -0.7803 0.0668 0.8298 3.4616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93156 0.08751 113.493 < 2e-16 ***
## category_code_LT01_1_count 0.39341 0.08615 4.567 6.27e-06 ***
## category_code_LT01_2_count 0.72856 0.07933 9.184 < 2e-16 ***
## category_code_LT01_3_count 0.38527 0.11126 3.463 0.000582 ***
## category_code_LT01_5_count 0.97622 0.06130 15.925 < 2e-16 ***
## category_code_LT01_12_count -0.04393 0.20733 -0.212 0.832283
## category_code_LT01_15_count -0.55563 0.76255 -0.729 0.466566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.625091941781429
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7778 0.0480 0.8303 3.4599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93322 0.08753 113.481 < 2e-16 ***
## category_code_LT01_1_count 0.38275 0.08509 4.498 8.56e-06 ***
## category_code_LT01_2_count 0.72704 0.07962 9.132 < 2e-16 ***
## category_code_LT01_3_count 0.37696 0.11121 3.389 0.000757 ***
## category_code_LT01_5_count 0.97674 0.06133 15.926 < 2e-16 ***
## category_code_LT01_12_count -0.03667 0.20723 -0.177 0.859627
## category_code_LT01_16_count -0.01693 1.17130 -0.014 0.988471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6251
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.625280594053302
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9939 -0.7689 0.0473 0.8287 3.4573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93585 0.08763 113.385 < 2e-16 ***
## category_code_LT01_1_count 0.37285 0.08624 4.323 1.86e-05 ***
## category_code_LT01_2_count 0.71874 0.07915 9.081 < 2e-16 ***
## category_code_LT01_3_count 0.37774 0.11062 3.415 0.000691 ***
## category_code_LT01_5_count 0.97115 0.06158 15.771 < 2e-16 ***
## category_code_LT01_13_count 0.03127 0.24444 0.128 0.898250
## category_code_LT01_14_count 0.16779 0.32661 0.514 0.607673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.625464893313837
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9923 -0.7801 0.0715 0.8307 3.4614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93178 0.08752 113.484 < 2e-16 ***
## category_code_LT01_1_count 0.39018 0.08666 4.502 8.4e-06 ***
## category_code_LT01_2_count 0.72555 0.07848 9.245 < 2e-16 ***
## category_code_LT01_3_count 0.38390 0.11112 3.455 0.000598 ***
## category_code_LT01_5_count 0.97475 0.06103 15.971 < 2e-16 ***
## category_code_LT01_13_count 0.01376 0.24538 0.056 0.955288
## category_code_LT01_15_count -0.54389 0.76487 -0.711 0.477363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.625079211648396
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9944 -0.7730 0.0482 0.8312 3.4597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.933460 0.087537 113.477 < 2e-16 ***
## category_code_LT01_1_count 0.379272 0.085461 4.438 1.12e-05 ***
## category_code_LT01_2_count 0.724112 0.078763 9.194 < 2e-16 ***
## category_code_LT01_3_count 0.375832 0.111078 3.383 0.000773 ***
## category_code_LT01_5_count 0.975316 0.061061 15.973 < 2e-16 ***
## category_code_LT01_13_count 0.029598 0.244655 0.121 0.903757
## category_code_LT01_16_count -0.007656 1.171938 -0.007 0.994790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6251
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.625660672497638
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9918 -0.7719 0.0593 0.8298 3.4591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93407 0.08761 113.392 < 2e-16 ***
## category_code_LT01_1_count 0.38467 0.08627 4.459 1.02e-05 ***
## category_code_LT01_2_count 0.72059 0.07895 9.127 < 2e-16 ***
## category_code_LT01_3_count 0.38594 0.11114 3.473 0.000561 ***
## category_code_LT01_5_count 0.97078 0.06150 15.785 < 2e-16 ***
## category_code_LT01_14_count 0.16643 0.32642 0.510 0.610390
## category_code_LT01_15_count -0.54643 0.76149 -0.718 0.473361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.625268278673247
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9940 -0.7700 0.0465 0.8284 3.4574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93573 0.08763 113.377 < 2e-16 ***
## category_code_LT01_1_count 0.37465 0.08528 4.393 1.37e-05 ***
## category_code_LT01_2_count 0.71932 0.07927 9.074 < 2e-16 ***
## category_code_LT01_3_count 0.37766 0.11109 3.399 0.00073 ***
## category_code_LT01_5_count 0.97148 0.06153 15.790 < 2e-16 ***
## category_code_LT01_14_count 0.16751 0.32702 0.512 0.60873
## category_code_LT01_16_count 0.01783 1.17236 0.015 0.98787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.625463769712387
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9923 -0.7800 0.0709 0.8306 3.4615
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93166 0.08752 113.483 < 2e-16 ***
## category_code_LT01_1_count 0.39082 0.08551 4.570 6.16e-06 ***
## category_code_LT01_2_count 0.72611 0.07856 9.243 < 2e-16 ***
## category_code_LT01_3_count 0.38444 0.11164 3.444 0.000623 ***
## category_code_LT01_5_count 0.97490 0.06098 15.986 < 2e-16 ***
## category_code_LT01_15_count -0.54909 0.76234 -0.720 0.471701
## category_code_LT01_16_count -0.04793 1.17151 -0.041 0.967382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.649125212334551
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9466 -0.7374 0.0372 0.8167 3.5032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92981 0.08473 117.192 < 2e-16 ***
## category_code_LT01_1_count 0.22439 0.08611 2.606 0.00944 **
## category_code_LT01_2_count 0.45276 0.08954 5.056 6.04e-07 ***
## category_code_LT01_4_count 0.49756 0.09991 4.980 8.81e-07 ***
## category_code_LT01_5_count 0.91164 0.06010 15.168 < 2e-16 ***
## category_code_LT01_6_count 0.28726 0.14880 1.931 0.05411 .
## category_code_LT01_7_count 0.37202 0.14986 2.483 0.01338 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.334 on 491 degrees of freedom
## Multiple R-squared: 0.6534, Adjusted R-squared: 0.6491
## F-statistic: 154.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.645003325320827
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9549 -0.7403 0.0774 0.8391 3.4890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92789 0.08525 116.460 < 2e-16 ***
## category_code_LT01_1_count 0.24391 0.08635 2.825 0.00492 **
## category_code_LT01_2_count 0.48171 0.08926 5.397 1.06e-07 ***
## category_code_LT01_4_count 0.54945 0.09829 5.590 3.77e-08 ***
## category_code_LT01_5_count 0.92633 0.06102 15.181 < 2e-16 ***
## category_code_LT01_6_count 0.28347 0.14975 1.893 0.05896 .
## category_code_LT01_8_count -0.16615 0.26593 -0.625 0.53241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6493, Adjusted R-squared: 0.645
## F-statistic: 151.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.646200575631786
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9426 -0.7192 0.0983 0.8729 3.5061
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92405 0.08509 116.630 < 2e-16 ***
## category_code_LT01_1_count 0.23711 0.08624 2.749 0.00619 **
## category_code_LT01_2_count 0.46678 0.08978 5.199 2.95e-07 ***
## category_code_LT01_4_count 0.54121 0.09828 5.507 5.90e-08 ***
## category_code_LT01_5_count 0.91423 0.06041 15.134 < 2e-16 ***
## category_code_LT01_6_count 0.27153 0.14950 1.816 0.06993 .
## category_code_LT01_9_count 0.31355 0.21882 1.433 0.15252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.34 on 491 degrees of freedom
## Multiple R-squared: 0.6505, Adjusted R-squared: 0.6462
## F-statistic: 152.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.645131247900668
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9322 -0.7576 0.0611 0.8733 3.4227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90905 0.08828 112.242 < 2e-16 ***
## category_code_LT01_1_count 0.24430 0.08634 2.830 0.00485 **
## category_code_LT01_2_count 0.47778 0.08947 5.340 1.42e-07 ***
## category_code_LT01_4_count 0.54553 0.09839 5.544 4.83e-08 ***
## category_code_LT01_5_count 0.92086 0.06033 15.263 < 2e-16 ***
## category_code_LT01_6_count 0.26256 0.15135 1.735 0.08340 .
## category_code_LT01_10_count 0.08297 0.11014 0.753 0.45162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6494, Adjusted R-squared: 0.6451
## F-statistic: 151.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.646229936556032
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9550 -0.7703 0.0688 0.8332 3.4924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93415 0.08523 116.550 < 2e-16 ***
## category_code_LT01_1_count 0.22265 0.08722 2.553 0.0110 *
## category_code_LT01_2_count 0.43342 0.09537 4.545 6.93e-06 ***
## category_code_LT01_4_count 0.50976 0.10184 5.006 7.78e-07 ***
## category_code_LT01_5_count 0.91748 0.06028 15.221 < 2e-16 ***
## category_code_LT01_6_count 0.25809 0.15013 1.719 0.0862 .
## category_code_LT01_11_count 0.16999 0.11747 1.447 0.1485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.34 on 491 degrees of freedom
## Multiple R-squared: 0.6505, Adjusted R-squared: 0.6462
## F-statistic: 152.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.644846884747332
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9507 -0.7603 0.0599 0.8714 3.4944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92611 0.08524 116.449 < 2e-16 ***
## category_code_LT01_1_count 0.24628 0.08686 2.835 0.00476 **
## category_code_LT01_2_count 0.48688 0.08984 5.419 9.39e-08 ***
## category_code_LT01_4_count 0.55019 0.09834 5.595 3.67e-08 ***
## category_code_LT01_5_count 0.92297 0.06061 15.229 < 2e-16 ***
## category_code_LT01_6_count 0.28552 0.15030 1.900 0.05806 .
## category_code_LT01_12_count -0.08441 0.20242 -0.417 0.67686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6491, Adjusted R-squared: 0.6448
## F-statistic: 151.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.644722919919072
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9495 -0.7640 0.0859 0.8533 3.4965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92642 0.08526 116.432 < 2e-16 ***
## category_code_LT01_1_count 0.24289 0.08717 2.786 0.00554 **
## category_code_LT01_2_count 0.48283 0.08936 5.404 1.02e-07 ***
## category_code_LT01_4_count 0.54947 0.09843 5.582 3.94e-08 ***
## category_code_LT01_5_count 0.92079 0.06041 15.243 < 2e-16 ***
## category_code_LT01_6_count 0.27955 0.14977 1.867 0.06255 .
## category_code_LT01_13_count -0.01194 0.23831 -0.050 0.96006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.649, Adjusted R-squared: 0.6447
## F-statistic: 151.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.644722503832821
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9495 -0.7631 0.0851 0.8577 3.4968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92666 0.08536 116.287 < 2e-16 ***
## category_code_LT01_1_count 0.24189 0.08681 2.786 0.00554 **
## category_code_LT01_2_count 0.48239 0.08948 5.391 1.09e-07 ***
## category_code_LT01_4_count 0.54875 0.09895 5.546 4.79e-08 ***
## category_code_LT01_5_count 0.92036 0.06080 15.138 < 2e-16 ***
## category_code_LT01_6_count 0.28051 0.15061 1.863 0.06312 .
## category_code_LT01_14_count 0.01413 0.32117 0.044 0.96493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.649, Adjusted R-squared: 0.6447
## F-statistic: 151.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.644925665735199
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9482 -0.7404 0.0846 0.8510 3.4980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92539 0.08525 116.427 < 2e-16 ***
## category_code_LT01_1_count 0.25018 0.08759 2.856 0.00447 **
## category_code_LT01_2_count 0.48457 0.08933 5.425 9.14e-08 ***
## category_code_LT01_4_count 0.54982 0.09830 5.593 3.71e-08 ***
## category_code_LT01_5_count 0.92041 0.06035 15.251 < 2e-16 ***
## category_code_LT01_6_count 0.28211 0.14972 1.884 0.06011 .
## category_code_LT01_15_count -0.39260 0.73818 -0.532 0.59507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6492, Adjusted R-squared: 0.6449
## F-statistic: 151.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.645000479475765
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9489 -0.7581 0.0715 0.8606 3.4977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92670 0.08522 116.486 < 2e-16 ***
## category_code_LT01_1_count 0.24339 0.08633 2.819 0.00501 **
## category_code_LT01_2_count 0.47486 0.09012 5.269 2.06e-07 ***
## category_code_LT01_4_count 0.54962 0.09829 5.592 3.73e-08 ***
## category_code_LT01_5_count 0.91949 0.06037 15.230 < 2e-16 ***
## category_code_LT01_6_count 0.28828 0.15026 1.919 0.05562 .
## category_code_LT01_16_count 0.70834 1.13952 0.622 0.53448
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6493, Adjusted R-squared: 0.645
## F-statistic: 151.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.646744314717797
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9684 -0.7283 0.0406 0.8353 3.4738
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93874 0.08497 116.972 < 2e-16 ***
## category_code_LT01_1_count 0.23740 0.08624 2.753 0.00613 **
## category_code_LT01_2_count 0.49192 0.08743 5.626 3.10e-08 ***
## category_code_LT01_4_count 0.53051 0.09879 5.370 1.22e-07 ***
## category_code_LT01_5_count 0.93028 0.06063 15.343 < 2e-16 ***
## category_code_LT01_7_count 0.36904 0.15040 2.454 0.01449 *
## category_code_LT01_8_count -0.16620 0.26520 -0.627 0.53115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.339 on 491 degrees of freedom
## Multiple R-squared: 0.651, Adjusted R-squared: 0.6467
## F-statistic: 152.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.647666953174414
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9564 -0.7188 0.0472 0.8078 3.4905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93465 0.08484 117.094 < 2e-16 ***
## category_code_LT01_1_count 0.23161 0.08615 2.689 0.00742 **
## category_code_LT01_2_count 0.47853 0.08798 5.439 8.47e-08 ***
## category_code_LT01_4_count 0.52454 0.09876 5.311 1.65e-07 ***
## category_code_LT01_5_count 0.91877 0.06002 15.308 < 2e-16 ***
## category_code_LT01_7_count 0.34884 0.15073 2.314 0.02106 *
## category_code_LT01_9_count 0.28390 0.21907 1.296 0.19559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.337 on 491 degrees of freedom
## Multiple R-squared: 0.6519, Adjusted R-squared: 0.6477
## F-statistic: 153.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.646979482858425
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9426 -0.7103 0.0279 0.8160 3.4006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91721 0.08811 112.554 < 2e-16 ***
## category_code_LT01_1_count 0.23772 0.08620 2.758 0.00604 **
## category_code_LT01_2_count 0.48527 0.08782 5.526 5.34e-08 ***
## category_code_LT01_4_count 0.52533 0.09893 5.310 1.66e-07 ***
## category_code_LT01_5_count 0.92411 0.05991 15.424 < 2e-16 ***
## category_code_LT01_7_count 0.35652 0.15071 2.366 0.01839 *
## category_code_LT01_10_count 0.09241 0.10890 0.849 0.39653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.338 on 491 degrees of freedom
## Multiple R-squared: 0.6512, Adjusted R-squared: 0.647
## F-statistic: 152.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.647390649642717
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9666 -0.7285 0.0456 0.8038 3.4790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94253 0.08498 116.994 < 2e-16 ***
## category_code_LT01_1_count 0.22116 0.08707 2.540 0.0114 *
## category_code_LT01_2_count 0.45376 0.09378 4.838 1.75e-06 ***
## category_code_LT01_4_count 0.50189 0.10181 4.930 1.13e-06 ***
## category_code_LT01_5_count 0.92208 0.05992 15.389 < 2e-16 ***
## category_code_LT01_7_count 0.32900 0.15371 2.140 0.0328 *
## category_code_LT01_11_count 0.13582 0.11942 1.137 0.2560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.337 on 491 degrees of freedom
## Multiple R-squared: 0.6516, Adjusted R-squared: 0.6474
## F-statistic: 153.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.646494569495124
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.7266 0.0496 0.8250 3.4803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93709 0.08496 116.958 < 2e-16 ***
## category_code_LT01_1_count 0.23793 0.08682 2.740 0.00636 **
## category_code_LT01_2_count 0.49517 0.08829 5.608 3.41e-08 ***
## category_code_LT01_4_count 0.53114 0.09891 5.370 1.22e-07 ***
## category_code_LT01_5_count 0.92584 0.06026 15.363 < 2e-16 ***
## category_code_LT01_7_count 0.36575 0.15040 2.432 0.01538 *
## category_code_LT01_12_count -0.04294 0.20112 -0.214 0.83101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.339 on 491 degrees of freedom
## Multiple R-squared: 0.6508, Adjusted R-squared: 0.6465
## F-statistic: 152.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.64655643726033
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9630 -0.7264 0.0427 0.8426 3.4813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93688 0.08496 116.962 < 2e-16 ***
## category_code_LT01_1_count 0.23979 0.08693 2.758 0.00603 **
## category_code_LT01_2_count 0.49322 0.08747 5.639 2.89e-08 ***
## category_code_LT01_4_count 0.53098 0.09884 5.372 1.20e-07 ***
## category_code_LT01_5_count 0.92514 0.05997 15.426 < 2e-16 ***
## category_code_LT01_7_count 0.37186 0.15119 2.460 0.01425 *
## category_code_LT01_13_count -0.08664 0.23888 -0.363 0.71699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.339 on 491 degrees of freedom
## Multiple R-squared: 0.6508, Adjusted R-squared: 0.6466
## F-statistic: 152.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.646525994210314
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9628 -0.7254 0.0418 0.8261 3.4810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93579 0.08509 116.772 < 2e-16 ***
## category_code_LT01_1_count 0.23813 0.08659 2.750 0.00618 **
## category_code_LT01_2_count 0.49349 0.08751 5.639 2.88e-08 ***
## category_code_LT01_4_count 0.53267 0.09915 5.372 1.20e-07 ***
## category_code_LT01_5_count 0.92640 0.06028 15.369 < 2e-16 ***
## category_code_LT01_7_count 0.36868 0.15062 2.448 0.01472 *
## category_code_LT01_14_count -0.09528 0.31892 -0.299 0.76526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.339 on 491 degrees of freedom
## Multiple R-squared: 0.6508, Adjusted R-squared: 0.6465
## F-statistic: 152.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.646553472200927
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9622 -0.7261 0.0424 0.8262 3.4824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93649 0.08498 116.931 < 2e-16 ***
## category_code_LT01_1_count 0.24124 0.08758 2.755 0.00609 **
## category_code_LT01_2_count 0.49429 0.08758 5.644 2.82e-08 ***
## category_code_LT01_4_count 0.53121 0.09886 5.374 1.19e-07 ***
## category_code_LT01_5_count 0.92448 0.05995 15.421 < 2e-16 ***
## category_code_LT01_7_count 0.36348 0.15056 2.414 0.01614 *
## category_code_LT01_15_count -0.26311 0.73709 -0.357 0.72127
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.339 on 491 degrees of freedom
## Multiple R-squared: 0.6508, Adjusted R-squared: 0.6466
## F-statistic: 152.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.646626199599012
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9628 -0.7271 0.0560 0.8254 3.4820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93755 0.08495 116.983 < 2e-16 ***
## category_code_LT01_1_count 0.23682 0.08624 2.746 0.00625 **
## category_code_LT01_2_count 0.48745 0.08810 5.533 5.13e-08 ***
## category_code_LT01_4_count 0.53118 0.09883 5.375 1.19e-07 ***
## category_code_LT01_5_count 0.92389 0.05996 15.409 < 2e-16 ***
## category_code_LT01_7_count 0.36699 0.15037 2.441 0.01502 *
## category_code_LT01_16_count 0.54125 1.13227 0.478 0.63285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.339 on 491 degrees of freedom
## Multiple R-squared: 0.6509, Adjusted R-squared: 0.6466
## F-statistic: 152.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.644084688662526
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9629 -0.7330 0.0884 0.8636 3.4789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93238 0.08529 116.453 < 2e-16 ***
## category_code_LT01_1_count 0.24895 0.08636 2.883 0.00411 **
## category_code_LT01_2_count 0.50250 0.08779 5.724 1.81e-08 ***
## category_code_LT01_4_count 0.57141 0.09720 5.879 7.65e-09 ***
## category_code_LT01_5_count 0.93146 0.06093 15.286 < 2e-16 ***
## category_code_LT01_8_count -0.15985 0.26622 -0.600 0.54849
## category_code_LT01_9_count 0.33327 0.21943 1.519 0.12947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6484, Adjusted R-squared: 0.6441
## F-statistic: 150.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.643195840716046
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9455 -0.7385 0.0965 0.8684 3.3692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91071 0.08854 111.932 < 2e-16 ***
## category_code_LT01_1_count 0.25657 0.08640 2.970 0.00313 **
## category_code_LT01_2_count 0.51070 0.08760 5.830 1.01e-08 ***
## category_code_LT01_4_count 0.57321 0.09740 5.885 7.38e-09 ***
## category_code_LT01_5_count 0.93767 0.06085 15.410 < 2e-16 ***
## category_code_LT01_8_count -0.15308 0.26648 -0.574 0.56592
## category_code_LT01_10_count 0.11337 0.10920 1.038 0.29972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 491 degrees of freedom
## Multiple R-squared: 0.6475, Adjusted R-squared: 0.6432
## F-statistic: 150.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.644288432392495
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9746 -0.7473 0.0680 0.8505 3.4664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94286 0.08539 116.446 < 2e-16 ***
## category_code_LT01_1_count 0.23181 0.08741 2.652 0.00826 **
## category_code_LT01_2_count 0.46270 0.09410 4.917 1.20e-06 ***
## category_code_LT01_4_count 0.53435 0.10119 5.281 1.94e-07 ***
## category_code_LT01_5_count 0.93324 0.06083 15.341 < 2e-16 ***
## category_code_LT01_8_count -0.13553 0.26608 -0.509 0.61073
## category_code_LT01_11_count 0.18865 0.11724 1.609 0.10824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.642449387422355
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9710 -0.7528 0.0758 0.8382 3.4674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93493 0.08547 116.238 < 2e-16 ***
## category_code_LT01_1_count 0.25690 0.08705 2.951 0.00332 **
## category_code_LT01_2_count 0.52331 0.08800 5.947 5.20e-09 ***
## category_code_LT01_4_count 0.58172 0.09730 5.979 4.33e-09 ***
## category_code_LT01_5_count 0.93953 0.06119 15.355 < 2e-16 ***
## category_code_LT01_8_count -0.14424 0.26684 -0.541 0.58906
## category_code_LT01_12_count -0.04544 0.20237 -0.225 0.82245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.64242728101654
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9704 -0.7509 0.0771 0.8440 3.4685
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93493 0.08548 116.231 < 2e-16 ***
## category_code_LT01_1_count 0.25636 0.08731 2.936 0.00348 **
## category_code_LT01_2_count 0.52102 0.08723 5.973 4.47e-09 ***
## category_code_LT01_4_count 0.58146 0.09732 5.975 4.43e-09 ***
## category_code_LT01_5_count 0.93860 0.06097 15.395 < 2e-16 ***
## category_code_LT01_8_count -0.14867 0.26721 -0.556 0.57820
## category_code_LT01_13_count -0.03390 0.23943 -0.142 0.88746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.642430043701705
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9702 -0.7499 0.0774 0.8536 3.4684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93431 0.08561 116.045 < 2e-16 ***
## category_code_LT01_1_count 0.25594 0.08687 2.946 0.00337 **
## category_code_LT01_2_count 0.52118 0.08726 5.973 4.48e-09 ***
## category_code_LT01_4_count 0.58229 0.09766 5.962 4.76e-09 ***
## category_code_LT01_5_count 0.93921 0.06124 15.335 < 2e-16 ***
## category_code_LT01_8_count -0.14582 0.26670 -0.547 0.58481
## category_code_LT01_14_count -0.04945 0.32028 -0.154 0.87735
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.642574826457957
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9692 -0.7489 0.0768 0.8364 3.4698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93415 0.08548 116.222 < 2e-16 ***
## category_code_LT01_1_count 0.26175 0.08775 2.983 0.003 **
## category_code_LT01_2_count 0.52260 0.08726 5.989 4.09e-09 ***
## category_code_LT01_4_count 0.58162 0.09722 5.982 4.24e-09 ***
## category_code_LT01_5_count 0.93805 0.06090 15.403 < 2e-16 ***
## category_code_LT01_8_count -0.14552 0.26664 -0.546 0.585
## category_code_LT01_15_count -0.34939 0.74032 -0.472 0.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6469, Adjusted R-squared: 0.6426
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.642578047093225
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9704 -0.7498 0.0737 0.8576 3.4688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93547 0.08546 116.262 < 2e-16 ***
## category_code_LT01_1_count 0.25582 0.08649 2.958 0.00325 **
## category_code_LT01_2_count 0.51550 0.08781 5.870 8.02e-09 ***
## category_code_LT01_4_count 0.58191 0.09723 5.985 4.18e-09 ***
## category_code_LT01_5_count 0.93784 0.06091 15.398 < 2e-16 ***
## category_code_LT01_8_count -0.15292 0.26699 -0.573 0.56707
## category_code_LT01_16_count 0.54344 1.14020 0.477 0.63384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6469, Adjusted R-squared: 0.6426
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.644352520467406
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9375 -0.7154 0.0845 0.8840 3.4034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91095 0.08838 112.135 < 2e-16 ***
## category_code_LT01_1_count 0.24925 0.08631 2.888 0.00405 **
## category_code_LT01_2_count 0.49619 0.08813 5.630 3.03e-08 ***
## category_code_LT01_4_count 0.56521 0.09739 5.803 1.17e-08 ***
## category_code_LT01_5_count 0.92579 0.06022 15.373 < 2e-16 ***
## category_code_LT01_9_count 0.30655 0.22078 1.389 0.16561
## category_code_LT01_10_count 0.09382 0.10976 0.855 0.39312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6444
## F-statistic: 151.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.645577326828343
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9626 -0.7283 0.0831 0.8561 3.4831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93874 0.08523 116.617 < 2e-16 ***
## category_code_LT01_1_count 0.22561 0.08725 2.586 0.010 *
## category_code_LT01_2_count 0.44794 0.09449 4.741 2.79e-06 ***
## category_code_LT01_4_count 0.52640 0.10111 5.206 2.84e-07 ***
## category_code_LT01_5_count 0.92183 0.06017 15.319 < 2e-16 ***
## category_code_LT01_9_count 0.31336 0.21908 1.430 0.153
## category_code_LT01_11_count 0.18255 0.11711 1.559 0.120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.341 on 491 degrees of freedom
## Multiple R-squared: 0.6499, Adjusted R-squared: 0.6456
## F-statistic: 151.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.643865537262317
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9586 -0.7237 0.0888 0.8723 3.4848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93084 0.08529 116.442 < 2e-16 ***
## category_code_LT01_1_count 0.24976 0.08693 2.873 0.00424 **
## category_code_LT01_2_count 0.50606 0.08863 5.710 1.96e-08 ***
## category_code_LT01_4_count 0.57185 0.09730 5.877 7.72e-09 ***
## category_code_LT01_5_count 0.92743 0.06057 15.312 < 2e-16 ***
## category_code_LT01_9_count 0.32876 0.21938 1.499 0.13462
## category_code_LT01_12_count -0.04868 0.20185 -0.241 0.80952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6482, Adjusted R-squared: 0.6439
## F-statistic: 150.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.643823349906131
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9577 -0.7221 0.0925 0.8756 3.4863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9309592 0.0852944 116.432 < 2e-16 ***
## category_code_LT01_1_count 0.2473370 0.0871916 2.837 0.00475 **
## category_code_LT01_2_count 0.5031638 0.0878969 5.724 1.81e-08 ***
## category_code_LT01_4_count 0.5709377 0.0973405 5.865 8.24e-09 ***
## category_code_LT01_5_count 0.9259602 0.0603116 15.353 < 2e-16 ***
## category_code_LT01_9_count 0.3288464 0.2199505 1.495 0.13553
## category_code_LT01_13_count -0.0001005 0.2391068 0.000 0.99966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6481, Adjusted R-squared: 0.6438
## F-statistic: 150.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.643868281239431
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9575 -0.7193 0.0914 0.8720 3.4858
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92974 0.08543 116.239 < 2e-16 ***
## category_code_LT01_1_count 0.24936 0.08672 2.875 0.00421 **
## category_code_LT01_2_count 0.50391 0.08786 5.735 1.70e-08 ***
## category_code_LT01_4_count 0.57314 0.09763 5.871 8.01e-09 ***
## category_code_LT01_5_count 0.92750 0.06058 15.311 < 2e-16 ***
## category_code_LT01_9_count 0.33204 0.21975 1.511 0.13144
## category_code_LT01_14_count -0.07969 0.32016 -0.249 0.80355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6482, Adjusted R-squared: 0.6439
## F-statistic: 150.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.643955691672612
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9568 -0.7197 0.0926 0.8708 3.4872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93017 0.08529 116.423 < 2e-16 ***
## category_code_LT01_1_count 0.25381 0.08765 2.896 0.00395 **
## category_code_LT01_2_count 0.50513 0.08792 5.745 1.61e-08 ***
## category_code_LT01_4_count 0.57171 0.09723 5.880 7.60e-09 ***
## category_code_LT01_5_count 0.92589 0.06025 15.366 < 2e-16 ***
## category_code_LT01_9_count 0.32577 0.21947 1.484 0.13836
## category_code_LT01_15_count -0.31582 0.73927 -0.427 0.66942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6483, Adjusted R-squared: 0.644
## F-statistic: 150.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.643939698072111
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9577 -0.7208 0.0884 0.8814 3.4865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93128 0.08528 116.456 < 2e-16 ***
## category_code_LT01_1_count 0.24830 0.08636 2.875 0.00422 **
## category_code_LT01_2_count 0.49905 0.08840 5.646 2.79e-08 ***
## category_code_LT01_4_count 0.57187 0.09725 5.881 7.56e-09 ***
## category_code_LT01_5_count 0.92550 0.06027 15.357 < 2e-16 ***
## category_code_LT01_9_count 0.32604 0.21947 1.486 0.13803
## category_code_LT01_16_count 0.45546 1.13707 0.401 0.68892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6482, Adjusted R-squared: 0.6439
## F-statistic: 150.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.644782438682889
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9467 -0.7165 0.0319 0.8641 3.3802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91874 0.08851 112.062 < 2e-16 ***
## category_code_LT01_1_count 0.23239 0.08732 2.661 0.00804 **
## category_code_LT01_2_count 0.45459 0.09440 4.816 1.96e-06 ***
## category_code_LT01_4_count 0.52727 0.10131 5.205 2.86e-07 ***
## category_code_LT01_5_count 0.92781 0.06007 15.446 < 2e-16 ***
## category_code_LT01_10_count 0.10582 0.10899 0.971 0.33208
## category_code_LT01_11_count 0.18620 0.11719 1.589 0.11273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6491, Adjusted R-squared: 0.6448
## F-statistic: 151.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.643014558011741
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9416 -0.7418 0.0995 0.8664 3.3750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90924 0.08856 111.897 < 2e-16 ***
## category_code_LT01_1_count 0.25780 0.08697 2.964 0.00318 **
## category_code_LT01_2_count 0.51454 0.08839 5.821 1.06e-08 ***
## category_code_LT01_4_count 0.57374 0.09749 5.885 7.37e-09 ***
## category_code_LT01_5_count 0.93405 0.06045 15.450 < 2e-16 ***
## category_code_LT01_10_count 0.11304 0.10928 1.034 0.30145
## category_code_LT01_12_count -0.05738 0.20225 -0.284 0.77674
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6473, Adjusted R-squared: 0.643
## F-statistic: 150.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.642969771004937
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9407 -0.7481 0.1039 0.8718 3.3772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90941 0.08857 111.885 < 2e-16 ***
## category_code_LT01_1_count 0.25655 0.08719 2.942 0.00341 **
## category_code_LT01_2_count 0.51161 0.08767 5.836 9.73e-09 ***
## category_code_LT01_4_count 0.57327 0.09751 5.879 7.62e-09 ***
## category_code_LT01_5_count 0.93260 0.06019 15.495 < 2e-16 ***
## category_code_LT01_10_count 0.11228 0.10925 1.028 0.30462
## category_code_LT01_13_count -0.03284 0.23889 -0.137 0.89071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6473, Adjusted R-squared: 0.643
## F-statistic: 150.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.643065819583225
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9386 -0.7426 0.0920 0.8849 3.3687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90574 0.08911 111.166 < 2e-16 ***
## category_code_LT01_1_count 0.25839 0.08683 2.976 0.00307 **
## category_code_LT01_2_count 0.51191 0.08763 5.842 9.42e-09 ***
## category_code_LT01_4_count 0.57577 0.09773 5.891 7.11e-09 ***
## category_code_LT01_5_count 0.93483 0.06049 15.454 < 2e-16 ***
## category_code_LT01_10_count 0.12110 0.11177 1.084 0.27911
## category_code_LT01_14_count -0.12728 0.32753 -0.389 0.69772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6474, Adjusted R-squared: 0.6431
## F-statistic: 150.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.643185358731358
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9384 -0.7402 0.0961 0.8666 3.3746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90748 0.08861 111.812 < 2e-16 ***
## category_code_LT01_1_count 0.26347 0.08769 3.005 0.00279 **
## category_code_LT01_2_count 0.51318 0.08767 5.854 8.80e-09 ***
## category_code_LT01_4_count 0.57329 0.09741 5.886 7.35e-09 ***
## category_code_LT01_5_count 0.93211 0.06014 15.500 < 2e-16 ***
## category_code_LT01_10_count 0.11688 0.10954 1.067 0.28649
## category_code_LT01_15_count -0.41692 0.74217 -0.562 0.57454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 491 degrees of freedom
## Multiple R-squared: 0.6475, Adjusted R-squared: 0.6432
## F-statistic: 150.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.643077809736378
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9411 -0.7477 0.1050 0.8772 3.3795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91031 0.08855 111.915 < 2e-16 ***
## category_code_LT01_1_count 0.25583 0.08640 2.961 0.00321 **
## category_code_LT01_2_count 0.50703 0.08822 5.747 1.59e-08 ***
## category_code_LT01_4_count 0.57373 0.09745 5.888 7.27e-09 ***
## category_code_LT01_5_count 0.93180 0.06016 15.489 < 2e-16 ***
## category_code_LT01_10_count 0.11014 0.10927 1.008 0.31397
## category_code_LT01_16_count 0.46607 1.13868 0.409 0.68250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6474, Adjusted R-squared: 0.6431
## F-statistic: 150.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.644378285164627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.7472 0.0491 0.8372 3.4687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94206 0.08535 116.490 < 2e-16 ***
## category_code_LT01_1_count 0.23460 0.08763 2.677 0.00767 **
## category_code_LT01_2_count 0.46514 0.09418 4.939 1.08e-06 ***
## category_code_LT01_4_count 0.53181 0.10120 5.255 2.21e-07 ***
## category_code_LT01_5_count 0.93192 0.06036 15.440 < 2e-16 ***
## category_code_LT01_11_count 0.20666 0.12018 1.720 0.08614 .
## category_code_LT01_12_count -0.12811 0.20686 -0.619 0.53599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6487, Adjusted R-squared: 0.6444
## F-statistic: 151.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.644115522659438
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9702 -0.7415 0.0503 0.8443 3.4727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94156 0.08538 116.438 < 2e-16 ***
## category_code_LT01_1_count 0.23185 0.08813 2.631 0.00878 **
## category_code_LT01_2_count 0.46291 0.09415 4.917 1.20e-06 ***
## category_code_LT01_4_count 0.53396 0.10126 5.273 2.01e-07 ***
## category_code_LT01_5_count 0.92874 0.06015 15.440 < 2e-16 ***
## category_code_LT01_11_count 0.19053 0.11726 1.625 0.10483
## category_code_LT01_13_count -0.03436 0.23846 -0.144 0.88550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6484, Adjusted R-squared: 0.6441
## F-statistic: 150.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.644116112108924
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9701 -0.7426 0.0644 0.8416 3.4725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94098 0.08551 116.252 < 2e-16 ***
## category_code_LT01_1_count 0.23144 0.08778 2.636 0.00864 **
## category_code_LT01_2_count 0.46319 0.09421 4.917 1.20e-06 ***
## category_code_LT01_4_count 0.53485 0.10164 5.262 2.13e-07 ***
## category_code_LT01_5_count 0.92941 0.06047 15.370 < 2e-16 ***
## category_code_LT01_11_count 0.19000 0.11723 1.621 0.10573
## category_code_LT01_14_count -0.04693 0.31952 -0.147 0.88330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6484, Adjusted R-squared: 0.6441
## F-statistic: 150.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.644284087461536
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9691 -0.7334 0.0584 0.8411 3.4740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94077 0.08538 116.436 < 2e-16 ***
## category_code_LT01_1_count 0.23764 0.08859 2.682 0.00756 **
## category_code_LT01_2_count 0.46442 0.09417 4.932 1.12e-06 ***
## category_code_LT01_4_count 0.53403 0.10118 5.278 1.97e-07 ***
## category_code_LT01_5_count 0.92827 0.06011 15.444 < 2e-16 ***
## category_code_LT01_11_count 0.19113 0.11722 1.631 0.10363
## category_code_LT01_15_count -0.37185 0.73863 -0.503 0.61489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.64424847177089
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9700 -0.7385 0.0588 0.8579 3.4732
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94202 0.08536 116.467 < 2e-16 ***
## category_code_LT01_1_count 0.23121 0.08739 2.646 0.00841 **
## category_code_LT01_2_count 0.45776 0.09471 4.833 1.80e-06 ***
## category_code_LT01_4_count 0.53440 0.10120 5.280 1.94e-07 ***
## category_code_LT01_5_count 0.92786 0.06012 15.433 < 2e-16 ***
## category_code_LT01_11_count 0.19026 0.11721 1.623 0.10517
## category_code_LT01_16_count 0.51342 1.13600 0.452 0.65151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6485, Adjusted R-squared: 0.6442
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.642244682179923
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9663 -0.7657 0.0777 0.8441 3.4741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93350 0.08547 116.227 < 2e-16 ***
## category_code_LT01_1_count 0.25679 0.08783 2.924 0.00362 **
## category_code_LT01_2_count 0.52422 0.08807 5.953 5.03e-09 ***
## category_code_LT01_4_count 0.58167 0.09741 5.972 4.51e-09 ***
## category_code_LT01_5_count 0.93480 0.06055 15.438 < 2e-16 ***
## category_code_LT01_12_count -0.04906 0.20232 -0.242 0.80850
## category_code_LT01_13_count -0.02516 0.23903 -0.105 0.91620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6466, Adjusted R-squared: 0.6422
## F-statistic: 149.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.642252333208346
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9662 -0.7566 0.0775 0.8514 3.4739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93290 0.08559 116.049 < 2e-16 ***
## category_code_LT01_1_count 0.25670 0.08738 2.938 0.00346 **
## category_code_LT01_2_count 0.52436 0.08807 5.954 4.99e-09 ***
## category_code_LT01_4_count 0.58256 0.09774 5.961 4.81e-09 ***
## category_code_LT01_5_count 0.93549 0.06083 15.380 < 2e-16 ***
## category_code_LT01_12_count -0.04747 0.20265 -0.234 0.81489
## category_code_LT01_14_count -0.04714 0.32089 -0.147 0.88326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6466, Adjusted R-squared: 0.6423
## F-statistic: 149.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.642408891992482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9653 -0.7529 0.0767 0.8370 3.4752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93266 0.08546 116.223 < 2e-16 ***
## category_code_LT01_1_count 0.26310 0.08839 2.977 0.00306 **
## category_code_LT01_2_count 0.52623 0.08813 5.971 4.52e-09 ***
## category_code_LT01_4_count 0.58210 0.09732 5.982 4.26e-09 ***
## category_code_LT01_5_count 0.93456 0.06050 15.446 < 2e-16 ***
## category_code_LT01_12_count -0.05352 0.20246 -0.264 0.79161
## category_code_LT01_15_count -0.36049 0.74118 -0.486 0.62692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.642380257832261
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9662 -0.7579 0.0784 0.8530 3.4745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93393 0.08545 116.259 < 2e-16 ***
## category_code_LT01_1_count 0.25651 0.08705 2.947 0.00336 **
## category_code_LT01_2_count 0.51911 0.08867 5.855 8.76e-09 ***
## category_code_LT01_4_count 0.58217 0.09733 5.982 4.26e-09 ***
## category_code_LT01_5_count 0.93398 0.06052 15.432 < 2e-16 ***
## category_code_LT01_12_count -0.04800 0.20229 -0.237 0.81253
## category_code_LT01_16_count 0.50587 1.13908 0.444 0.65716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.642221128243848
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9654 -0.7528 0.0786 0.8598 3.4752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93283 0.08560 116.033 < 2e-16 ***
## category_code_LT01_1_count 0.25576 0.08768 2.917 0.0037 **
## category_code_LT01_2_count 0.52193 0.08733 5.976 4.39e-09 ***
## category_code_LT01_4_count 0.58226 0.09778 5.955 4.96e-09 ***
## category_code_LT01_5_count 0.93438 0.06060 15.420 < 2e-16 ***
## category_code_LT01_13_count -0.02623 0.23907 -0.110 0.9127
## category_code_LT01_14_count -0.05213 0.32040 -0.163 0.8708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6465, Adjusted R-squared: 0.6422
## F-statistic: 149.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.642374550928851
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9644 -0.7507 0.0787 0.8421 3.4766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93263 0.08547 116.208 < 2e-16 ***
## category_code_LT01_1_count 0.26226 0.08873 2.956 0.00327 **
## category_code_LT01_2_count 0.52351 0.08735 5.993 3.98e-09 ***
## category_code_LT01_4_count 0.58172 0.09734 5.976 4.39e-09 ***
## category_code_LT01_5_count 0.93326 0.06023 15.494 < 2e-16 ***
## category_code_LT01_13_count -0.03618 0.23998 -0.151 0.88022
## category_code_LT01_15_count -0.36209 0.74358 -0.487 0.62651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.642345269524231
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9653 -0.7629 0.0793 0.8618 3.4759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93395 0.08546 116.246 < 2e-16 ***
## category_code_LT01_1_count 0.25520 0.08727 2.924 0.00361 **
## category_code_LT01_2_count 0.51653 0.08790 5.876 7.74e-09 ***
## category_code_LT01_4_count 0.58164 0.09734 5.975 4.42e-09 ***
## category_code_LT01_5_count 0.93271 0.06025 15.481 < 2e-16 ***
## category_code_LT01_13_count -0.02174 0.23915 -0.091 0.92759
## category_code_LT01_16_count 0.50577 1.13977 0.444 0.65742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6423
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.642377826722176
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9643 -0.7463 0.0784 0.8517 3.4764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93202 0.08560 116.028 < 2e-16 ***
## category_code_LT01_1_count 0.26166 0.08815 2.968 0.00314 **
## category_code_LT01_2_count 0.52362 0.08736 5.994 3.98e-09 ***
## category_code_LT01_4_count 0.58260 0.09768 5.964 4.70e-09 ***
## category_code_LT01_5_count 0.93402 0.06055 15.427 < 2e-16 ***
## category_code_LT01_14_count -0.05285 0.32030 -0.165 0.86901
## category_code_LT01_15_count -0.35297 0.74053 -0.477 0.63383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.642353035570763
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9653 -0.7450 0.0768 0.8632 3.4757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93338 0.08559 116.063 < 2e-16 ***
## category_code_LT01_1_count 0.25526 0.08686 2.939 0.00345 **
## category_code_LT01_2_count 0.51681 0.08794 5.877 7.72e-09 ***
## category_code_LT01_4_count 0.58253 0.09768 5.963 4.73e-09 ***
## category_code_LT01_5_count 0.93343 0.06057 15.411 < 2e-16 ***
## category_code_LT01_14_count -0.04413 0.32074 -0.138 0.89063
## category_code_LT01_16_count 0.50126 1.14059 0.439 0.66051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6467, Adjusted R-squared: 0.6424
## F-statistic: 149.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.642494413231076
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9643 -0.7498 0.0752 0.8520 3.4770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93315 0.08545 116.240 < 2e-16 ***
## category_code_LT01_1_count 0.26105 0.08774 2.975 0.00307 **
## category_code_LT01_2_count 0.51833 0.08793 5.895 6.99e-09 ***
## category_code_LT01_4_count 0.58198 0.09725 5.984 4.19e-09 ***
## category_code_LT01_5_count 0.93239 0.06020 15.488 < 2e-16 ***
## category_code_LT01_15_count -0.34195 0.74074 -0.462 0.64455
## category_code_LT01_16_count 0.49316 1.13935 0.433 0.66532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6425
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.631773506343725
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9926 -0.7776 0.0329 0.9056 3.4551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93803 0.08682 114.464 < 2e-16 ***
## category_code_LT01_1_count 0.36929 0.08318 4.440 1.11e-05 ***
## category_code_LT01_2_count 0.68120 0.07863 8.664 < 2e-16 ***
## category_code_LT01_5_count 0.96621 0.06144 15.726 < 2e-16 ***
## category_code_LT01_6_count 0.41719 0.15034 2.775 0.005732 **
## category_code_LT01_7_count 0.53084 0.15022 3.534 0.000449 ***
## category_code_LT01_8_count -0.19098 0.27097 -0.705 0.481271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6362, Adjusted R-squared: 0.6318
## F-statistic: 143.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.632814602214101
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9795 -0.7836 0.0444 0.8708 3.4594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93375 0.08669 114.591 < 2e-16 ***
## category_code_LT01_1_count 0.36197 0.08313 4.354 1.63e-05 ***
## category_code_LT01_2_count 0.66619 0.07940 8.391 5.18e-16 ***
## category_code_LT01_5_count 0.95345 0.06086 15.667 < 2e-16 ***
## category_code_LT01_6_count 0.40315 0.15018 2.684 0.007511 **
## category_code_LT01_7_count 0.50694 0.15068 3.364 0.000827 ***
## category_code_LT01_9_count 0.30751 0.22366 1.375 0.169795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.365 on 491 degrees of freedom
## Multiple R-squared: 0.6372, Adjusted R-squared: 0.6328
## F-statistic: 143.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.631775716795183
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9698 -0.7762 0.0384 0.9248 3.4736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91956 0.08999 110.234 < 2e-16 ***
## category_code_LT01_1_count 0.36931 0.08318 4.440 1.11e-05 ***
## category_code_LT01_2_count 0.67760 0.07892 8.585 < 2e-16 ***
## category_code_LT01_5_count 0.95998 0.06077 15.797 < 2e-16 ***
## category_code_LT01_6_count 0.39567 0.15220 2.600 0.009612 **
## category_code_LT01_7_count 0.51814 0.15073 3.437 0.000637 ***
## category_code_LT01_10_count 0.07952 0.11249 0.707 0.479973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6362, Adjusted R-squared: 0.6318
## F-statistic: 143.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.63438303709401
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9910 -0.7622 0.0336 0.8717 3.4472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94594 0.08661 114.830 < 2e-16 ***
## category_code_LT01_1_count 0.32997 0.08496 3.884 0.000117 ***
## category_code_LT01_2_count 0.59757 0.08912 6.705 5.54e-11 ***
## category_code_LT01_5_count 0.95246 0.06066 15.701 < 2e-16 ***
## category_code_LT01_6_count 0.36953 0.15125 2.443 0.014913 *
## category_code_LT01_7_count 0.44720 0.15490 2.887 0.004062 **
## category_code_LT01_11_count 0.23838 0.11912 2.001 0.045923 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.362 on 491 degrees of freedom
## Multiple R-squared: 0.6388, Adjusted R-squared: 0.6344
## F-statistic: 144.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.631452173382581
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9874 -0.7787 0.0353 0.9070 3.4570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93614 0.08683 114.429 < 2e-16 ***
## category_code_LT01_1_count 0.37041 0.08385 4.417 1.23e-05 ***
## category_code_LT01_2_count 0.68570 0.07949 8.626 < 2e-16 ***
## category_code_LT01_5_count 0.96136 0.06108 15.740 < 2e-16 ***
## category_code_LT01_6_count 0.41676 0.15100 2.760 0.005996 **
## category_code_LT01_7_count 0.52728 0.15022 3.510 0.000489 ***
## category_code_LT01_12_count -0.05384 0.20615 -0.261 0.794075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6315
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.631426128739117
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9867 -0.7742 0.0387 0.9087 3.4569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93623 0.08683 114.427 < 2e-16 ***
## category_code_LT01_1_count 0.36988 0.08408 4.399 1.33e-05 ***
## category_code_LT01_2_count 0.68331 0.07872 8.681 < 2e-16 ***
## category_code_LT01_5_count 0.96021 0.06083 15.784 < 2e-16 ***
## category_code_LT01_6_count 0.41225 0.15034 2.742 0.006327 **
## category_code_LT01_7_count 0.53057 0.15115 3.510 0.000489 ***
## category_code_LT01_13_count -0.04466 0.24396 -0.183 0.854836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6314
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.631515820832025
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9860 -0.7912 0.0407 0.9162 3.4551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93803 0.08692 114.329 < 2e-16 ***
## category_code_LT01_1_count 0.36308 0.08399 4.323 1.86e-05 ***
## category_code_LT01_2_count 0.67872 0.07927 8.562 < 2e-16 ***
## category_code_LT01_5_count 0.95665 0.06133 15.599 < 2e-16 ***
## category_code_LT01_6_count 0.41844 0.15093 2.772 0.005776 **
## category_code_LT01_7_count 0.52303 0.15064 3.472 0.000562 ***
## category_code_LT01_14_count 0.12752 0.32598 0.391 0.695828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.636, Adjusted R-squared: 0.6315
## F-statistic: 143 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.631468253857865
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9859 -0.7749 0.0421 0.9047 3.4574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93573 0.08685 114.398 < 2e-16 ***
## category_code_LT01_1_count 0.37248 0.08473 4.396 1.35e-05 ***
## category_code_LT01_2_count 0.68425 0.07881 8.682 < 2e-16 ***
## category_code_LT01_5_count 0.95978 0.06079 15.788 < 2e-16 ***
## category_code_LT01_6_count 0.41440 0.15036 2.756 0.006070 **
## category_code_LT01_7_count 0.52544 0.15037 3.494 0.000518 ***
## category_code_LT01_15_count -0.22538 0.75278 -0.299 0.764770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6315
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.631695092464362
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9859 -0.7741 0.0425 0.9116 3.4565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93662 0.08680 114.476 < 2e-16 ***
## category_code_LT01_1_count 0.36878 0.08317 4.434 1.14e-05 ***
## category_code_LT01_2_count 0.67466 0.07964 8.471 2.84e-16 ***
## category_code_LT01_5_count 0.95859 0.06081 15.765 < 2e-16 ***
## category_code_LT01_6_count 0.42174 0.15089 2.795 0.005394 **
## category_code_LT01_7_count 0.52886 0.15018 3.522 0.000469 ***
## category_code_LT01_16_count 0.72685 1.16078 0.626 0.531491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6361, Adjusted R-squared: 0.6317
## F-statistic: 143.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.624666398470587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9934 -0.7980 0.0709 0.9539 3.4623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93084 0.08767 113.282 < 2e-16 ***
## category_code_LT01_1_count 0.40850 0.08300 4.922 1.17e-06 ***
## category_code_LT01_2_count 0.73685 0.07734 9.528 < 2e-16 ***
## category_code_LT01_5_count 0.97761 0.06197 15.776 < 2e-16 ***
## category_code_LT01_6_count 0.41244 0.15194 2.714 0.00687 **
## category_code_LT01_8_count -0.17599 0.27358 -0.643 0.52033
## category_code_LT01_9_count 0.38688 0.22513 1.718 0.08634 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6247
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.623196186737118
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9780 -0.8159 0.0214 0.9566 3.4835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90969 0.09099 108.909 < 2e-16 ***
## category_code_LT01_1_count 0.41909 0.08299 5.050 6.25e-07 ***
## category_code_LT01_2_count 0.75216 0.07670 9.807 < 2e-16 ***
## category_code_LT01_5_count 0.98601 0.06189 15.932 < 2e-16 ***
## category_code_LT01_6_count 0.39979 0.15407 2.595 0.00974 **
## category_code_LT01_8_count -0.16612 0.27402 -0.606 0.54465
## category_code_LT01_10_count 0.11483 0.11337 1.013 0.31163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.628377246628856
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0055 -0.7960 0.0495 0.8646 3.4456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94753 0.08735 113.881 < 2e-16 ***
## category_code_LT01_1_count 0.35605 0.08530 4.174 3.54e-05 ***
## category_code_LT01_2_count 0.62875 0.08918 7.050 6.09e-12 ***
## category_code_LT01_5_count 0.97104 0.06170 15.739 < 2e-16 ***
## category_code_LT01_6_count 0.36330 0.15260 2.381 0.01766 *
## category_code_LT01_8_count -0.14003 0.27219 -0.514 0.60716
## category_code_LT01_11_count 0.32585 0.11604 2.808 0.00518 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6329, Adjusted R-squared: 0.6284
## F-statistic: 141.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.622461089440733
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0030 -0.8069 0.0230 0.9515 3.4596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93357 0.08791 112.997 < 2e-16 ***
## category_code_LT01_1_count 0.42062 0.08373 5.024 7.11e-07 ***
## category_code_LT01_2_count 0.76459 0.07710 9.916 < 2e-16 ***
## category_code_LT01_5_count 0.98760 0.06221 15.876 < 2e-16 ***
## category_code_LT01_6_count 0.42863 0.15292 2.803 0.00526 **
## category_code_LT01_8_count -0.15864 0.27437 -0.578 0.56339
## category_code_LT01_12_count -0.05437 0.20875 -0.260 0.79462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6225
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.622430865212944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0020 -0.8045 0.0340 0.9389 3.4592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93393 0.08791 112.999 < 2e-16 ***
## category_code_LT01_1_count 0.41550 0.08425 4.932 1.12e-06 ***
## category_code_LT01_2_count 0.76053 0.07645 9.948 < 2e-16 ***
## category_code_LT01_5_count 0.98556 0.06205 15.884 < 2e-16 ***
## category_code_LT01_6_count 0.42535 0.15226 2.794 0.00541 **
## category_code_LT01_8_count -0.15803 0.27477 -0.575 0.56546
## category_code_LT01_13_count 0.04153 0.24586 0.169 0.86594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6224
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.622742533286341
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0013 -0.8070 0.0344 0.9506 3.4564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93671 0.08798 112.939 < 2e-16 ***
## category_code_LT01_1_count 0.40945 0.08401 4.874 1.48e-06 ***
## category_code_LT01_2_count 0.75368 0.07711 9.774 < 2e-16 ***
## category_code_LT01_5_count 0.98055 0.06251 15.687 < 2e-16 ***
## category_code_LT01_6_count 0.43412 0.15281 2.841 0.00469 **
## category_code_LT01_8_count -0.16387 0.27418 -0.598 0.55034
## category_code_LT01_14_count 0.21674 0.32893 0.659 0.51025
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6227
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.622565862485016
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0011 -0.7958 0.0357 0.9506 3.4603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93286 0.08792 112.979 < 2e-16 ***
## category_code_LT01_1_count 0.42494 0.08451 5.028 6.96e-07 ***
## category_code_LT01_2_count 0.76350 0.07632 10.003 < 2e-16 ***
## category_code_LT01_5_count 0.98593 0.06194 15.917 < 2e-16 ***
## category_code_LT01_6_count 0.42698 0.15227 2.804 0.00525 **
## category_code_LT01_8_count -0.16023 0.27421 -0.584 0.55925
## category_code_LT01_15_count -0.34386 0.76103 -0.452 0.65159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6271, Adjusted R-squared: 0.6226
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.62268764558517
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0020 -0.7921 0.0262 0.9506 3.4590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93412 0.08788 113.041 < 2e-16 ***
## category_code_LT01_1_count 0.41919 0.08307 5.046 6.35e-07 ***
## category_code_LT01_2_count 0.75390 0.07724 9.760 < 2e-16 ***
## category_code_LT01_5_count 0.98530 0.06195 15.906 < 2e-16 ***
## category_code_LT01_6_count 0.43363 0.15287 2.837 0.00475 **
## category_code_LT01_8_count -0.17010 0.27459 -0.619 0.53590
## category_code_LT01_16_count 0.70860 1.17660 0.602 0.54729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6272, Adjusted R-squared: 0.6227
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.624853440900703
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9687 -0.7942 0.0706 0.9597 3.4831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91009 0.09078 109.171 < 2e-16 ***
## category_code_LT01_1_count 0.40828 0.08296 4.921 1.17e-06 ***
## category_code_LT01_2_count 0.73185 0.07767 9.423 < 2e-16 ***
## category_code_LT01_5_count 0.97196 0.06127 15.864 < 2e-16 ***
## category_code_LT01_6_count 0.38908 0.15369 2.532 0.0117 *
## category_code_LT01_9_count 0.36085 0.22650 1.593 0.1118
## category_code_LT01_10_count 0.09243 0.11387 0.812 0.4174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6249
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.630002336187957
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9924 -0.7983 0.0740 0.8543 3.4500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94319 0.08716 114.085 < 2e-16 ***
## category_code_LT01_1_count 0.34763 0.08516 4.082 5.21e-05 ***
## category_code_LT01_2_count 0.61071 0.08974 6.805 2.95e-11 ***
## category_code_LT01_5_count 0.95849 0.06104 15.703 < 2e-16 ***
## category_code_LT01_6_count 0.35025 0.15224 2.301 0.02183 *
## category_code_LT01_9_count 0.34825 0.22375 1.556 0.12026
## category_code_LT01_11_count 0.31748 0.11592 2.739 0.00639 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.624406895191087
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9887 -0.7983 0.0736 0.9598 3.4640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92914 0.08767 113.256 < 2e-16 ***
## category_code_LT01_1_count 0.40971 0.08366 4.897 1.32e-06 ***
## category_code_LT01_2_count 0.74121 0.07822 9.476 < 2e-16 ***
## category_code_LT01_5_count 0.97333 0.06159 15.803 < 2e-16 ***
## category_code_LT01_6_count 0.41265 0.15260 2.704 0.00708 **
## category_code_LT01_9_count 0.38198 0.22509 1.697 0.09033 .
## category_code_LT01_12_count -0.05673 0.20811 -0.273 0.78529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.624428397954583
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9873 -0.7952 0.0845 0.9443 3.4635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92962 0.08767 113.266 < 2e-16 ***
## category_code_LT01_1_count 0.40227 0.08419 4.778 2.34e-06 ***
## category_code_LT01_2_count 0.73576 0.07766 9.474 < 2e-16 ***
## category_code_LT01_5_count 0.97070 0.06139 15.813 < 2e-16 ***
## category_code_LT01_6_count 0.40959 0.15191 2.696 0.00725 **
## category_code_LT01_9_count 0.38708 0.22560 1.716 0.08683 .
## category_code_LT01_13_count 0.07851 0.24531 0.320 0.74909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.624569078187665
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9871 -0.7960 0.0771 0.9532 3.4614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93177 0.08776 113.168 < 2e-16 ***
## category_code_LT01_1_count 0.40016 0.08390 4.769 2.44e-06 ***
## category_code_LT01_2_count 0.73218 0.07809 9.376 < 2e-16 ***
## category_code_LT01_5_count 0.96728 0.06185 15.639 < 2e-16 ***
## category_code_LT01_6_count 0.41639 0.15254 2.730 0.00657 **
## category_code_LT01_9_count 0.37393 0.22558 1.658 0.09803 .
## category_code_LT01_14_count 0.17601 0.32886 0.535 0.59275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.624472163776059
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9869 -0.7914 0.0796 0.9345 3.4646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92857 0.08768 113.235 < 2e-16 ***
## category_code_LT01_1_count 0.41314 0.08448 4.891 1.36e-06 ***
## category_code_LT01_2_count 0.73993 0.07748 9.550 < 2e-16 ***
## category_code_LT01_5_count 0.97162 0.06130 15.850 < 2e-16 ***
## category_code_LT01_6_count 0.41064 0.15195 2.702 0.00712 **
## category_code_LT01_9_count 0.37922 0.22520 1.684 0.09283 .
## category_code_LT01_15_count -0.30348 0.75952 -0.400 0.68965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.624549273635598
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9874 -0.7942 0.0849 0.9594 3.4636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92960 0.08765 113.287 < 2e-16 ***
## category_code_LT01_1_count 0.40795 0.08300 4.915 1.21e-06 ***
## category_code_LT01_2_count 0.73186 0.07826 9.351 < 2e-16 ***
## category_code_LT01_5_count 0.97084 0.06132 15.833 < 2e-16 ***
## category_code_LT01_6_count 0.41602 0.15254 2.727 0.00661 **
## category_code_LT01_9_count 0.37817 0.22519 1.679 0.09372 .
## category_code_LT01_16_count 0.59851 1.17259 0.510 0.60999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.628813323149982
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9790 -0.7970 0.0652 0.8665 3.4686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92452 0.09047 109.698 < 2e-16 ***
## category_code_LT01_1_count 0.35601 0.08520 4.178 3.48e-05 ***
## category_code_LT01_2_count 0.62179 0.08946 6.951 1.16e-11 ***
## category_code_LT01_5_count 0.96606 0.06094 15.854 < 2e-16 ***
## category_code_LT01_6_count 0.33786 0.15423 2.191 0.02895 *
## category_code_LT01_10_count 0.10328 0.11256 0.918 0.35933
## category_code_LT01_11_count 0.32401 0.11599 2.793 0.00542 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6333, Adjusted R-squared: 0.6288
## F-statistic: 141.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.622985908154494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9737 -0.8116 0.0193 0.9515 3.4851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90808 0.09101 108.870 < 2e-16 ***
## category_code_LT01_1_count 0.42063 0.08365 5.029 6.94e-07 ***
## category_code_LT01_2_count 0.75659 0.07755 9.757 < 2e-16 ***
## category_code_LT01_5_count 0.98214 0.06148 15.975 < 2e-16 ***
## category_code_LT01_6_count 0.40064 0.15465 2.591 0.00986 **
## category_code_LT01_10_count 0.11445 0.11342 1.009 0.31343
## category_code_LT01_12_count -0.06376 0.20858 -0.306 0.75997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6275, Adjusted R-squared: 0.623
## F-statistic: 137.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.622935767382688
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.8122 0.0224 0.9521 3.4843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90884 0.09102 108.865 < 2e-16 ***
## category_code_LT01_1_count 0.41505 0.08413 4.934 1.11e-06 ***
## category_code_LT01_2_count 0.75214 0.07693 9.777 < 2e-16 ***
## category_code_LT01_5_count 0.97987 0.06126 15.994 < 2e-16 ***
## category_code_LT01_6_count 0.39707 0.15408 2.577 0.0103 *
## category_code_LT01_10_count 0.11282 0.11347 0.994 0.3206
## category_code_LT01_13_count 0.04117 0.24541 0.168 0.8668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6275, Adjusted R-squared: 0.6229
## F-statistic: 137.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.623051674346558
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9747 -0.7990 0.0152 0.9377 3.4802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91292 0.09157 108.250 < 2e-16 ***
## category_code_LT01_1_count 0.41167 0.08405 4.898 1.32e-06 ***
## category_code_LT01_2_count 0.74887 0.07735 9.682 < 2e-16 ***
## category_code_LT01_5_count 0.97661 0.06182 15.797 < 2e-16 ***
## category_code_LT01_6_count 0.40500 0.15535 2.607 0.00941 **
## category_code_LT01_10_count 0.10177 0.11672 0.872 0.38370
## category_code_LT01_14_count 0.14325 0.33847 0.423 0.67231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6276, Adjusted R-squared: 0.6231
## F-statistic: 137.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.623134703178391
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9706 -0.8057 0.0266 0.9395 3.4867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90641 0.09106 108.784 < 2e-16 ***
## category_code_LT01_1_count 0.42584 0.08444 5.043 6.46e-07 ***
## category_code_LT01_2_count 0.75502 0.07677 9.834 < 2e-16 ***
## category_code_LT01_5_count 0.98010 0.06119 16.019 < 2e-16 ***
## category_code_LT01_6_count 0.39786 0.15400 2.584 0.0101 *
## category_code_LT01_10_count 0.11834 0.11371 1.041 0.2985
## category_code_LT01_15_count -0.40891 0.76283 -0.536 0.5922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6231
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.623119711926786
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9731 -0.7995 0.0212 0.9592 3.4838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90939 0.09099 108.901 < 2e-16 ***
## category_code_LT01_1_count 0.41840 0.08298 5.042 6.48e-07 ***
## category_code_LT01_2_count 0.74683 0.07765 9.618 < 2e-16 ***
## category_code_LT01_5_count 0.97936 0.06121 15.999 < 2e-16 ***
## category_code_LT01_6_count 0.40438 0.15476 2.613 0.00925 **
## category_code_LT01_10_count 0.11047 0.11351 0.973 0.33095
## category_code_LT01_16_count 0.60841 1.17568 0.517 0.60504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6231
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.628793748623977
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0042 -0.7874 0.0435 0.8566 3.4466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94659 0.08727 113.969 < 2e-16 ***
## category_code_LT01_1_count 0.35972 0.08540 4.212 3.01e-05 ***
## category_code_LT01_2_count 0.63016 0.08915 7.069 5.39e-12 ***
## category_code_LT01_5_count 0.97060 0.06113 15.877 < 2e-16 ***
## category_code_LT01_6_count 0.36911 0.15271 2.417 0.0160 *
## category_code_LT01_11_count 0.35030 0.11865 2.952 0.0033 **
## category_code_LT01_12_count -0.19127 0.21175 -0.903 0.3668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6333, Adjusted R-squared: 0.6288
## F-statistic: 141.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62818457207903
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7939 0.0580 0.8745 3.4467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94647 0.08735 113.871 < 2e-16 ***
## category_code_LT01_1_count 0.35302 0.08623 4.094 4.96e-05 ***
## category_code_LT01_2_count 0.62836 0.08931 7.036 6.69e-12 ***
## category_code_LT01_5_count 0.96592 0.06104 15.824 < 2e-16 ***
## category_code_LT01_6_count 0.36027 0.15255 2.362 0.01859 *
## category_code_LT01_11_count 0.32704 0.11611 2.817 0.00505 **
## category_code_LT01_13_count 0.02450 0.24370 0.101 0.91997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6327, Adjusted R-squared: 0.6282
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.628417682105748
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0001 -0.7940 0.0520 0.8751 3.4444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94876 0.08742 113.807 < 2e-16 ***
## category_code_LT01_1_count 0.34752 0.08608 4.037 6.27e-05 ***
## category_code_LT01_2_count 0.62298 0.08977 6.939 1.25e-11 ***
## category_code_LT01_5_count 0.96144 0.06155 15.621 < 2e-16 ***
## category_code_LT01_6_count 0.36807 0.15314 2.403 0.01661 *
## category_code_LT01_11_count 0.32539 0.11605 2.804 0.00525 **
## category_code_LT01_14_count 0.18419 0.32656 0.564 0.57299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6329, Adjusted R-squared: 0.6284
## F-statistic: 141.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.628369355868758
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9998 -0.7926 0.0584 0.8669 3.4478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94540 0.08735 113.862 < 2e-16 ***
## category_code_LT01_1_count 0.36197 0.08658 4.181 3.44e-05 ***
## category_code_LT01_2_count 0.63056 0.08925 7.065 5.53e-12 ***
## category_code_LT01_5_count 0.96594 0.06097 15.842 < 2e-16 ***
## category_code_LT01_6_count 0.36206 0.15252 2.374 0.01799 *
## category_code_LT01_11_count 0.32843 0.11601 2.831 0.00483 **
## category_code_LT01_15_count -0.38082 0.75524 -0.504 0.61433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6329, Adjusted R-squared: 0.6284
## F-statistic: 141.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.628413087839055
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0005 -0.7945 0.0562 0.8794 3.4465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94661 0.08732 113.910 < 2e-16 ***
## category_code_LT01_1_count 0.35550 0.08526 4.170 3.61e-05 ***
## category_code_LT01_2_count 0.62194 0.09002 6.909 1.52e-11 ***
## category_code_LT01_5_count 0.96514 0.06100 15.823 < 2e-16 ***
## category_code_LT01_6_count 0.36780 0.15311 2.402 0.01667 *
## category_code_LT01_11_count 0.32715 0.11599 2.820 0.00499 **
## category_code_LT01_16_count 0.65126 1.16583 0.559 0.57667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6329, Adjusted R-squared: 0.6284
## F-statistic: 141.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.622237146927411
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9977 -0.8140 0.0402 0.9390 3.4608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93237 0.08791 112.985 < 2e-16 ***
## category_code_LT01_1_count 0.41634 0.08484 4.908 1.26e-06 ***
## category_code_LT01_2_count 0.76436 0.07734 9.883 < 2e-16 ***
## category_code_LT01_5_count 0.98172 0.06160 15.936 < 2e-16 ***
## category_code_LT01_6_count 0.42608 0.15290 2.787 0.00553 **
## category_code_LT01_12_count -0.05860 0.20872 -0.281 0.77902
## category_code_LT01_13_count 0.05093 0.24548 0.207 0.83572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6222
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.62254962697988
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9970 -0.8186 0.0401 0.9565 3.4581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93509 0.08797 112.932 < 2e-16 ***
## category_code_LT01_1_count 0.41107 0.08456 4.862 1.57e-06 ***
## category_code_LT01_2_count 0.75815 0.07787 9.736 < 2e-16 ***
## category_code_LT01_5_count 0.97677 0.06207 15.738 < 2e-16 ***
## category_code_LT01_6_count 0.43545 0.15354 2.836 0.00476 **
## category_code_LT01_12_count -0.06812 0.20916 -0.326 0.74479
## category_code_LT01_14_count 0.22113 0.32980 0.670 0.50286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6271, Adjusted R-squared: 0.6225
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.6223725902739
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9969 -0.7987 0.0376 0.9474 3.4620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93120 0.08791 112.965 < 2e-16 ***
## category_code_LT01_1_count 0.42676 0.08525 5.006 7.76e-07 ***
## category_code_LT01_2_count 0.76787 0.07724 9.941 < 2e-16 ***
## category_code_LT01_5_count 0.98223 0.06153 15.963 < 2e-16 ***
## category_code_LT01_6_count 0.42790 0.15293 2.798 0.00534 **
## category_code_LT01_12_count -0.06267 0.20890 -0.300 0.76432
## category_code_LT01_15_count -0.35675 0.76203 -0.468 0.63988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6269, Adjusted R-squared: 0.6224
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.622451158061254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9974 -0.8007 0.0454 0.9537 3.4607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93242 0.08788 113.022 < 2e-16 ***
## category_code_LT01_1_count 0.42031 0.08372 5.021 7.22e-07 ***
## category_code_LT01_2_count 0.75842 0.07812 9.709 < 2e-16 ***
## category_code_LT01_5_count 0.98120 0.06155 15.940 < 2e-16 ***
## category_code_LT01_6_count 0.43336 0.15347 2.824 0.00494 **
## category_code_LT01_12_count -0.05750 0.20866 -0.276 0.78299
## category_code_LT01_16_count 0.66621 1.17515 0.567 0.57103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6225
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.622503425684216
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9958 -0.8120 0.0444 0.9500 3.4577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93547 0.08798 112.925 < 2e-16 ***
## category_code_LT01_1_count 0.40489 0.08517 4.754 2.62e-06 ***
## category_code_LT01_2_count 0.75328 0.07738 9.735 < 2e-16 ***
## category_code_LT01_5_count 0.97440 0.06191 15.739 < 2e-16 ***
## category_code_LT01_6_count 0.43109 0.15277 2.822 0.00497 **
## category_code_LT01_13_count 0.05262 0.24541 0.214 0.83032
## category_code_LT01_14_count 0.21455 0.32902 0.652 0.51465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6271, Adjusted R-squared: 0.6225
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.62232418729394
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9958 -0.8025 0.0441 0.9311 3.4615
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93166 0.08792 112.961 < 2e-16 ***
## category_code_LT01_1_count 0.42084 0.08587 4.901 1.3e-06 ***
## category_code_LT01_2_count 0.76324 0.07661 9.962 < 2e-16 ***
## category_code_LT01_5_count 0.98000 0.06131 15.983 < 2e-16 ***
## category_code_LT01_6_count 0.42389 0.15223 2.785 0.00557 **
## category_code_LT01_13_count 0.04053 0.24642 0.164 0.86942
## category_code_LT01_15_count -0.33491 0.76432 -0.438 0.66145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6269, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.622432375789058
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9963 -0.8032 0.0468 0.9440 3.4603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93286 0.08788 113.024 < 2e-16 ***
## category_code_LT01_1_count 0.41426 0.08418 4.921 1.18e-06 ***
## category_code_LT01_2_count 0.75369 0.07752 9.722 < 2e-16 ***
## category_code_LT01_5_count 0.97890 0.06134 15.959 < 2e-16 ***
## category_code_LT01_6_count 0.43023 0.15282 2.815 0.00507 **
## category_code_LT01_13_count 0.05574 0.24559 0.227 0.82054
## category_code_LT01_16_count 0.67840 1.17607 0.577 0.56431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6224
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.622625216872946
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9949 -0.8172 0.0406 0.9456 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93432 0.08799 112.905 < 2e-16 ***
## category_code_LT01_1_count 0.41501 0.08544 4.857 1.6e-06 ***
## category_code_LT01_2_count 0.75659 0.07723 9.796 < 2e-16 ***
## category_code_LT01_5_count 0.97487 0.06183 15.766 < 2e-16 ***
## category_code_LT01_6_count 0.43244 0.15278 2.831 0.00484 **
## category_code_LT01_14_count 0.21285 0.32893 0.647 0.51787
## category_code_LT01_15_count -0.34408 0.76096 -0.452 0.65135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6272, Adjusted R-squared: 0.6226
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.62275493549188
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9954 -0.8096 0.0505 0.9612 3.4575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93568 0.08795 112.969 < 2e-16 ***
## category_code_LT01_1_count 0.40864 0.08398 4.866 1.54e-06 ***
## category_code_LT01_2_count 0.74645 0.07825 9.539 < 2e-16 ***
## category_code_LT01_5_count 0.97354 0.06187 15.735 < 2e-16 ***
## category_code_LT01_6_count 0.43956 0.15343 2.865 0.00435 **
## category_code_LT01_14_count 0.22625 0.32954 0.687 0.49268
## category_code_LT01_16_count 0.71920 1.17703 0.611 0.54147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6228
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.622540744744698
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9955 -0.8012 0.0445 0.9493 3.4614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93174 0.08789 113.002 < 2e-16 ***
## category_code_LT01_1_count 0.42424 0.08449 5.021 7.2e-07 ***
## category_code_LT01_2_count 0.75724 0.07737 9.788 < 2e-16 ***
## category_code_LT01_5_count 0.97941 0.06126 15.987 < 2e-16 ***
## category_code_LT01_6_count 0.43124 0.15281 2.822 0.00496 **
## category_code_LT01_15_count -0.33404 0.76135 -0.439 0.66104
## category_code_LT01_16_count 0.65317 1.17547 0.556 0.57869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6271, Adjusted R-squared: 0.6225
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.627733676670726
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0119 -0.7936 0.0637 0.9106 3.4468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94637 0.08722 114.034 < 2e-16 ***
## category_code_LT01_1_count 0.39311 0.08301 4.735 2.86e-06 ***
## category_code_LT01_2_count 0.74303 0.07446 9.979 < 2e-16 ***
## category_code_LT01_5_count 0.98195 0.06140 15.993 < 2e-16 ***
## category_code_LT01_7_count 0.51411 0.15177 3.387 0.000762 ***
## category_code_LT01_8_count -0.17361 0.27237 -0.637 0.524160
## category_code_LT01_9_count 0.34045 0.22506 1.513 0.131001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6322, Adjusted R-squared: 0.6277
## F-statistic: 140.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.626995221520172
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9914 -0.7689 0.0492 0.9069 3.4715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92168 0.09059 109.523 < 2e-16 ***
## category_code_LT01_1_count 0.40075 0.08297 4.830 1.83e-06 ***
## category_code_LT01_2_count 0.74979 0.07426 10.097 < 2e-16 ***
## category_code_LT01_5_count 0.98808 0.06128 16.123 < 2e-16 ***
## category_code_LT01_7_count 0.52143 0.15177 3.436 0.000642 ***
## category_code_LT01_8_count -0.16782 0.27257 -0.616 0.538388
## category_code_LT01_10_count 0.12801 0.11177 1.145 0.252653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6315, Adjusted R-squared: 0.627
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.630142775974733
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0211 -0.7471 0.0278 0.8560 3.4341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95902 0.08701 114.455 < 2e-16 ***
## category_code_LT01_1_count 0.35212 0.08511 4.137 4.14e-05 ***
## category_code_LT01_2_count 0.65421 0.08658 7.556 2.05e-13 ***
## category_code_LT01_5_count 0.97701 0.06124 15.955 < 2e-16 ***
## category_code_LT01_7_count 0.44195 0.15588 2.835 0.00477 **
## category_code_LT01_8_count -0.14138 0.27148 -0.521 0.60275
## category_code_LT01_11_count 0.27824 0.11863 2.345 0.01940 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6346, Adjusted R-squared: 0.6301
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.625999325062828
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0199 -0.7723 0.0276 0.9174 3.4437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.949460 0.087404 113.833 < 2e-16 ***
## category_code_LT01_1_count 0.399760 0.083884 4.766 2.48e-06 ***
## category_code_LT01_2_count 0.763051 0.074832 10.197 < 2e-16 ***
## category_code_LT01_5_count 0.989010 0.061691 16.032 < 2e-16 ***
## category_code_LT01_7_count 0.536849 0.151382 3.546 0.000428 ***
## category_code_LT01_8_count -0.161068 0.273037 -0.590 0.555519
## category_code_LT01_12_count 0.005545 0.206824 0.027 0.978620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.626062482328558
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0203 -0.7760 0.0264 0.9121 3.4439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94924 0.08740 113.839 < 2e-16 ***
## category_code_LT01_1_count 0.40361 0.08397 4.807 2.04e-06 ***
## category_code_LT01_2_count 0.76425 0.07344 10.406 < 2e-16 ***
## category_code_LT01_5_count 0.98992 0.06140 16.121 < 2e-16 ***
## category_code_LT01_7_count 0.54179 0.15234 3.557 0.000412 ***
## category_code_LT01_8_count -0.16603 0.27344 -0.607 0.544002
## category_code_LT01_13_count -0.07120 0.24619 -0.289 0.772535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.626013415857638
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0199 -0.7710 0.0300 0.9087 3.4431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95009 0.08753 113.682 < 2e-16 ***
## category_code_LT01_1_count 0.39860 0.08375 4.759 2.56e-06 ***
## category_code_LT01_2_count 0.76242 0.07376 10.337 < 2e-16 ***
## category_code_LT01_5_count 0.98818 0.06178 15.995 < 2e-16 ***
## category_code_LT01_7_count 0.53527 0.15179 3.526 0.000461 ***
## category_code_LT01_8_count -0.16121 0.27288 -0.591 0.554951
## category_code_LT01_14_count 0.04533 0.32699 0.139 0.889799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.626030524478665
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.7631 0.0279 0.9128 3.4441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94904 0.08742 113.805 < 2e-16 ***
## category_code_LT01_1_count 0.40346 0.08471 4.763 2.52e-06 ***
## category_code_LT01_2_count 0.76473 0.07366 10.382 < 2e-16 ***
## category_code_LT01_5_count 0.98920 0.06135 16.123 < 2e-16 ***
## category_code_LT01_7_count 0.53542 0.15153 3.533 0.000449 ***
## category_code_LT01_8_count -0.16037 0.27287 -0.588 0.556979
## category_code_LT01_15_count -0.15474 0.75794 -0.204 0.838312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.626118255971327
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0202 -0.7709 0.0272 0.9204 3.4433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94982 0.08739 113.851 < 2e-16 ***
## category_code_LT01_1_count 0.40126 0.08312 4.828 1.85e-06 ***
## category_code_LT01_2_count 0.75944 0.07408 10.252 < 2e-16 ***
## category_code_LT01_5_count 0.98892 0.06135 16.119 < 2e-16 ***
## category_code_LT01_7_count 0.53788 0.15138 3.553 0.000417 ***
## category_code_LT01_8_count -0.16647 0.27320 -0.609 0.542587
## category_code_LT01_16_count 0.46188 1.16604 0.396 0.692194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.628136188183716
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9824 -0.7862 0.0826 0.9410 3.4718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92138 0.09043 109.711 < 2e-16 ***
## category_code_LT01_1_count 0.39246 0.08293 4.732 2.91e-06 ***
## category_code_LT01_2_count 0.73379 0.07513 9.767 < 2e-16 ***
## category_code_LT01_5_count 0.97544 0.06066 16.080 < 2e-16 ***
## category_code_LT01_7_count 0.49983 0.15209 3.286 0.00109 **
## category_code_LT01_9_count 0.31087 0.22632 1.374 0.17020
## category_code_LT01_10_count 0.10879 0.11231 0.969 0.33322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6326, Adjusted R-squared: 0.6281
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.631438573725333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0084 -0.7620 0.0343 0.8641 3.4386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95457 0.08686 114.610 < 2e-16 ***
## category_code_LT01_1_count 0.34482 0.08498 4.058 5.77e-05 ***
## category_code_LT01_2_count 0.63745 0.08721 7.309 1.10e-12 ***
## category_code_LT01_5_count 0.96513 0.06057 15.934 < 2e-16 ***
## category_code_LT01_7_count 0.41959 0.15608 2.688 0.00743 **
## category_code_LT01_9_count 0.31664 0.22399 1.414 0.15810
## category_code_LT01_11_count 0.27387 0.11845 2.312 0.02118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6314
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.627425657007581
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0062 -0.7901 0.0646 0.9158 3.4484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9447921 0.0872252 114.013 < 2e-16 ***
## category_code_LT01_1_count 0.3913699 0.0838204 4.669 3.91e-06 ***
## category_code_LT01_2_count 0.7438076 0.0758764 9.803 < 2e-16 ***
## category_code_LT01_5_count 0.9759858 0.0610874 15.977 < 2e-16 ***
## category_code_LT01_7_count 0.5113284 0.1517744 3.369 0.000814 ***
## category_code_LT01_9_count 0.3359930 0.2250463 1.493 0.136081
## category_code_LT01_12_count 0.0008947 0.2063026 0.004 0.996541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.627438617321568
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0063 -0.7897 0.0626 0.9146 3.4485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94470 0.08723 114.011 < 2e-16 ***
## category_code_LT01_1_count 0.39304 0.08393 4.683 3.66e-06 ***
## category_code_LT01_2_count 0.74438 0.07458 9.981 < 2e-16 ***
## category_code_LT01_5_count 0.97632 0.06076 16.068 < 2e-16 ***
## category_code_LT01_7_count 0.51368 0.15284 3.361 0.000837 ***
## category_code_LT01_9_count 0.33363 0.22577 1.478 0.140123
## category_code_LT01_13_count -0.03217 0.24600 -0.131 0.896015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.62742737761026
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0062 -0.7936 0.0652 0.9163 3.4481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94502 0.08735 113.847 < 2e-16 ***
## category_code_LT01_1_count 0.39092 0.08365 4.673 3.84e-06 ***
## category_code_LT01_2_count 0.74356 0.07476 9.945 < 2e-16 ***
## category_code_LT01_5_count 0.97568 0.06112 15.963 < 2e-16 ***
## category_code_LT01_7_count 0.51083 0.15213 3.358 0.000846 ***
## category_code_LT01_9_count 0.33538 0.22541 1.488 0.137429
## category_code_LT01_14_count 0.01563 0.32688 0.048 0.961883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.627447148999044
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0059 -0.7929 0.0673 0.9135 3.4487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94447 0.08724 113.986 < 2e-16 ***
## category_code_LT01_1_count 0.39423 0.08467 4.656 4.15e-06 ***
## category_code_LT01_2_count 0.74499 0.07477 9.964 < 2e-16 ***
## category_code_LT01_5_count 0.97606 0.06071 16.076 < 2e-16 ***
## category_code_LT01_7_count 0.51024 0.15191 3.359 0.000843 ***
## category_code_LT01_9_count 0.33496 0.22512 1.488 0.137426
## category_code_LT01_15_count -0.12741 0.75677 -0.168 0.866372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.627502095952998
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0062 -0.7848 0.0690 0.9188 3.4481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94507 0.08722 114.023 < 2e-16 ***
## category_code_LT01_1_count 0.39237 0.08305 4.724 3.02e-06 ***
## category_code_LT01_2_count 0.74082 0.07508 9.867 < 2e-16 ***
## category_code_LT01_5_count 0.97570 0.06072 16.069 < 2e-16 ***
## category_code_LT01_7_count 0.51224 0.15178 3.375 0.000797 ***
## category_code_LT01_9_count 0.33369 0.22514 1.482 0.138944
## category_code_LT01_16_count 0.36916 1.16290 0.317 0.751036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.632, Adjusted R-squared: 0.6275
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.630783566151106
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9899 -0.7279 0.0509 0.8474 3.4612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93198 0.09024 110.059 < 2e-16 ***
## category_code_LT01_1_count 0.35162 0.08498 4.138 4.12e-05 ***
## category_code_LT01_2_count 0.64300 0.08711 7.381 6.76e-13 ***
## category_code_LT01_5_count 0.97094 0.06044 16.065 < 2e-16 ***
## category_code_LT01_7_count 0.42591 0.15610 2.728 0.00659 **
## category_code_LT01_10_count 0.11792 0.11124 1.060 0.28962
## category_code_LT01_11_count 0.27598 0.11853 2.328 0.02030 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6352, Adjusted R-squared: 0.6308
## F-statistic: 142.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.626708585699552
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7739 0.0607 0.9182 3.4728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.920391 0.090615 109.479 < 2e-16 ***
## category_code_LT01_1_count 0.399517 0.083777 4.769 2.45e-06 ***
## category_code_LT01_2_count 0.751093 0.075596 9.936 < 2e-16 ***
## category_code_LT01_5_count 0.982542 0.060942 16.123 < 2e-16 ***
## category_code_LT01_7_count 0.518580 0.151770 3.417 0.000686 ***
## category_code_LT01_10_count 0.126666 0.111890 1.132 0.258160
## category_code_LT01_12_count -0.008665 0.206687 -0.042 0.966577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6267
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.626765199230473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7741 0.0555 0.9201 3.4731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92006 0.09061 109.484 < 2e-16 ***
## category_code_LT01_1_count 0.40235 0.08382 4.800 2.11e-06 ***
## category_code_LT01_2_count 0.75121 0.07432 10.108 < 2e-16 ***
## category_code_LT01_5_count 0.98279 0.06060 16.218 < 2e-16 ***
## category_code_LT01_7_count 0.52318 0.15264 3.427 0.000661 ***
## category_code_LT01_10_count 0.12718 0.11181 1.137 0.255899
## category_code_LT01_13_count -0.06778 0.24549 -0.276 0.782583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.62671677929086
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9856 -0.7753 0.0576 0.9191 3.4738
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91932 0.09116 108.812 < 2e-16 ***
## category_code_LT01_1_count 0.40026 0.08368 4.783 2.29e-06 ***
## category_code_LT01_2_count 0.75105 0.07444 10.089 < 2e-16 ***
## category_code_LT01_5_count 0.98307 0.06100 16.115 < 2e-16 ***
## category_code_LT01_7_count 0.51959 0.15200 3.418 0.000683 ***
## category_code_LT01_10_count 0.12921 0.11444 1.129 0.259413
## category_code_LT01_14_count -0.03744 0.33440 -0.112 0.910901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6267
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.626779961907043
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9849 -0.7727 0.0555 0.9187 3.4740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91918 0.09069 109.377 < 2e-16 ***
## category_code_LT01_1_count 0.40419 0.08461 4.777 2.35e-06 ***
## category_code_LT01_2_count 0.75213 0.07446 10.101 < 2e-16 ***
## category_code_LT01_5_count 0.98228 0.06057 16.218 < 2e-16 ***
## category_code_LT01_7_count 0.51613 0.15197 3.396 0.000738 ***
## category_code_LT01_10_count 0.12951 0.11221 1.154 0.248998
## category_code_LT01_15_count -0.23508 0.76008 -0.309 0.757234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.626786427981819
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9865 -0.7732 0.0627 0.9232 3.4721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92101 0.09061 109.492 < 2e-16 ***
## category_code_LT01_1_count 0.39994 0.08299 4.819 1.93e-06 ***
## category_code_LT01_2_count 0.74741 0.07489 9.980 < 2e-16 ***
## category_code_LT01_5_count 0.98190 0.06058 16.209 < 2e-16 ***
## category_code_LT01_7_count 0.51957 0.15178 3.423 0.000671 ***
## category_code_LT01_10_count 0.12511 0.11186 1.118 0.263909
## category_code_LT01_16_count 0.37576 1.16423 0.323 0.747022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.630173681330082
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0189 -0.7476 0.0237 0.8549 3.4351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95807 0.08698 114.490 < 2e-16 ***
## category_code_LT01_1_count 0.35430 0.08533 4.152 3.88e-05 ***
## category_code_LT01_2_count 0.65602 0.08665 7.571 1.85e-13 ***
## category_code_LT01_5_count 0.97521 0.06075 16.053 < 2e-16 ***
## category_code_LT01_7_count 0.43303 0.15610 2.774 0.00575 **
## category_code_LT01_11_count 0.29597 0.12191 2.428 0.01555 *
## category_code_LT01_12_count -0.11810 0.21134 -0.559 0.57655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6346, Adjusted R-squared: 0.6302
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.629990918049703
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0165 -0.7471 0.0340 0.8550 3.4356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95754 0.08700 114.453 < 2e-16 ***
## category_code_LT01_1_count 0.35347 0.08588 4.116 4.52e-05 ***
## category_code_LT01_2_count 0.65461 0.08663 7.556 2.05e-13 ***
## category_code_LT01_5_count 0.97251 0.06052 16.068 < 2e-16 ***
## category_code_LT01_7_count 0.44315 0.15666 2.829 0.00487 **
## category_code_LT01_11_count 0.28029 0.11860 2.363 0.01850 *
## category_code_LT01_13_count -0.06446 0.24437 -0.264 0.79205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.629952214263953
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0163 -0.7480 0.0347 0.8609 3.4348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95839 0.08712 114.300 < 2e-16 ***
## category_code_LT01_1_count 0.34892 0.08572 4.070 5.47e-05 ***
## category_code_LT01_2_count 0.65294 0.08691 7.513 2.76e-13 ***
## category_code_LT01_5_count 0.97103 0.06093 15.936 < 2e-16 ***
## category_code_LT01_7_count 0.43728 0.15621 2.799 0.00532 **
## category_code_LT01_11_count 0.28014 0.11860 2.362 0.01857 *
## category_code_LT01_14_count 0.04391 0.32525 0.135 0.89267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.630001463989091
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0159 -0.7423 0.0348 0.8591 3.4359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95724 0.08702 114.431 < 2e-16 ***
## category_code_LT01_1_count 0.35493 0.08652 4.102 4.79e-05 ***
## category_code_LT01_2_count 0.65530 0.08672 7.556 2.05e-13 ***
## category_code_LT01_5_count 0.97201 0.06049 16.068 < 2e-16 ***
## category_code_LT01_7_count 0.43643 0.15601 2.798 0.00535 **
## category_code_LT01_11_count 0.28127 0.11866 2.370 0.01816 *
## category_code_LT01_15_count -0.21808 0.75431 -0.289 0.77262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.630045586513917
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0164 -0.7476 0.0356 0.8613 3.4351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95807 0.08699 114.468 < 2e-16 ***
## category_code_LT01_1_count 0.35139 0.08510 4.129 4.28e-05 ***
## category_code_LT01_2_count 0.65009 0.08719 7.456 4.06e-13 ***
## category_code_LT01_5_count 0.97157 0.06050 16.059 < 2e-16 ***
## category_code_LT01_7_count 0.43963 0.15580 2.822 0.00497 **
## category_code_LT01_11_count 0.28032 0.11859 2.364 0.01848 *
## category_code_LT01_16_count 0.43673 1.15833 0.377 0.70631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.625781751210846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0147 -0.7845 0.0381 0.9101 3.4454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.947734 0.087396 113.824 < 2e-16 ***
## category_code_LT01_1_count 0.401328 0.084717 4.737 2.84e-06 ***
## category_code_LT01_2_count 0.764555 0.074889 10.209 < 2e-16 ***
## category_code_LT01_5_count 0.984020 0.061040 16.121 < 2e-16 ***
## category_code_LT01_7_count 0.538167 0.152278 3.534 0.000448 ***
## category_code_LT01_12_count 0.001744 0.206766 0.008 0.993272
## category_code_LT01_13_count -0.061354 0.245759 -0.250 0.802962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.625747590392587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7754 0.0448 0.8992 3.4446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9485635 0.0875186 113.674 < 2e-16 ***
## category_code_LT01_1_count 0.3970353 0.0844661 4.701 3.38e-06 ***
## category_code_LT01_2_count 0.7630294 0.0751203 10.157 < 2e-16 ***
## category_code_LT01_5_count 0.9826366 0.0614047 16.003 < 2e-16 ***
## category_code_LT01_7_count 0.5324732 0.1517773 3.508 0.000493 ***
## category_code_LT01_12_count -0.0004348 0.2071683 -0.002 0.998326
## category_code_LT01_14_count 0.0433565 0.3277234 0.132 0.894804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.625767430327759
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7679 0.0387 0.9103 3.4456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.947532 0.087416 113.795 < 2e-16 ***
## category_code_LT01_1_count 0.401926 0.085608 4.695 3.46e-06 ***
## category_code_LT01_2_count 0.765329 0.075162 10.182 < 2e-16 ***
## category_code_LT01_5_count 0.983644 0.061014 16.122 < 2e-16 ***
## category_code_LT01_7_count 0.532529 0.151508 3.515 0.000481 ***
## category_code_LT01_12_count -0.000601 0.206956 -0.003 0.997684
## category_code_LT01_15_count -0.158342 0.758900 -0.209 0.834810
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.625835633516252
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7757 0.0441 0.9231 3.4449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.948245 0.087389 113.838 < 2e-16 ***
## category_code_LT01_1_count 0.399334 0.083911 4.759 2.56e-06 ***
## category_code_LT01_2_count 0.760139 0.075535 10.063 < 2e-16 ***
## category_code_LT01_5_count 0.983094 0.061019 16.111 < 2e-16 ***
## category_code_LT01_7_count 0.534848 0.151356 3.534 0.000449 ***
## category_code_LT01_12_count 0.002354 0.206764 0.011 0.990921
## category_code_LT01_16_count 0.424936 1.165010 0.365 0.715456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.625794011038461
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0147 -0.7821 0.0416 0.9035 3.4448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94832 0.08752 113.671 < 2e-16 ***
## category_code_LT01_1_count 0.40004 0.08463 4.727 2.98e-06 ***
## category_code_LT01_2_count 0.76374 0.07384 10.344 < 2e-16 ***
## category_code_LT01_5_count 0.98314 0.06111 16.087 < 2e-16 ***
## category_code_LT01_7_count 0.53668 0.15272 3.514 0.000482 ***
## category_code_LT01_13_count -0.06067 0.24580 -0.247 0.805161
## category_code_LT01_14_count 0.04158 0.32714 0.127 0.898904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.625822344713586
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0143 -0.7702 0.0366 0.9043 3.4459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94725 0.08741 113.794 < 2e-16 ***
## category_code_LT01_1_count 0.40551 0.08576 4.728 2.96e-06 ***
## category_code_LT01_2_count 0.76619 0.07375 10.389 < 2e-16 ***
## category_code_LT01_5_count 0.98415 0.06067 16.223 < 2e-16 ***
## category_code_LT01_7_count 0.53690 0.15237 3.524 0.000465 ***
## category_code_LT01_13_count -0.06621 0.24664 -0.268 0.788462
## category_code_LT01_15_count -0.17574 0.76092 -0.231 0.817449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.625878281420877
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0147 -0.7813 0.0427 0.9158 3.4451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94803 0.08739 113.838 < 2e-16 ***
## category_code_LT01_1_count 0.40229 0.08395 4.792 2.19e-06 ***
## category_code_LT01_2_count 0.76106 0.07415 10.263 < 2e-16 ***
## category_code_LT01_5_count 0.98364 0.06067 16.212 < 2e-16 ***
## category_code_LT01_7_count 0.53880 0.15227 3.539 0.000441 ***
## category_code_LT01_13_count -0.05824 0.24587 -0.237 0.812867
## category_code_LT01_16_count 0.41497 1.16555 0.356 0.721970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.625780634158229
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7664 0.0462 0.8958 3.4450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94815 0.08754 113.644 < 2e-16 ***
## category_code_LT01_1_count 0.40048 0.08538 4.691 3.53e-06 ***
## category_code_LT01_2_count 0.76432 0.07405 10.322 < 2e-16 ***
## category_code_LT01_5_count 0.98266 0.06108 16.089 < 2e-16 ***
## category_code_LT01_7_count 0.53105 0.15192 3.496 0.000516 ***
## category_code_LT01_14_count 0.04306 0.32708 0.132 0.895312
## category_code_LT01_15_count -0.15788 0.75818 -0.208 0.835135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.625853163188985
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0145 -0.7713 0.0502 0.9008 3.4442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94896 0.08751 113.686 < 2e-16 ***
## category_code_LT01_1_count 0.39787 0.08376 4.750 2.67e-06 ***
## category_code_LT01_2_count 0.75911 0.07451 10.189 < 2e-16 ***
## category_code_LT01_5_count 0.98205 0.06109 16.075 < 2e-16 ***
## category_code_LT01_7_count 0.53314 0.15176 3.513 0.000484 ***
## category_code_LT01_14_count 0.04981 0.32751 0.152 0.879171
## category_code_LT01_16_count 0.43421 1.16652 0.372 0.709888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.625865141698079
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7654 0.0434 0.9149 3.4453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94784 0.08741 113.810 < 2e-16 ***
## category_code_LT01_1_count 0.40272 0.08472 4.753 2.63e-06 ***
## category_code_LT01_2_count 0.76161 0.07438 10.239 < 2e-16 ***
## category_code_LT01_5_count 0.98321 0.06064 16.214 < 2e-16 ***
## category_code_LT01_7_count 0.53347 0.15151 3.521 0.00047 ***
## category_code_LT01_15_count -0.14951 0.75848 -0.197 0.84382
## category_code_LT01_16_count 0.41736 1.16545 0.358 0.72042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.620199715703549
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9893 -0.7725 0.0266 0.9206 3.4810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91212 0.09136 108.499 < 2e-16 ***
## category_code_LT01_1_count 0.43881 0.08274 5.303 1.72e-07 ***
## category_code_LT01_2_count 0.80202 0.07293 10.997 < 2e-16 ***
## category_code_LT01_5_count 0.99883 0.06179 16.164 < 2e-16 ***
## category_code_LT01_8_count -0.15419 0.27506 -0.561 0.5753
## category_code_LT01_9_count 0.38182 0.22791 1.675 0.0945 .
## category_code_LT01_10_count 0.13894 0.11319 1.228 0.2202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.626180185628742
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0200 -0.7762 0.0687 0.8472 3.4379
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95528 0.08750 113.769 < 2e-16 ***
## category_code_LT01_1_count 0.36776 0.08529 4.312 1.96e-05 ***
## category_code_LT01_2_count 0.66266 0.08733 7.588 1.64e-13 ***
## category_code_LT01_5_count 0.98079 0.06161 15.918 < 2e-16 ***
## category_code_LT01_8_count -0.12752 0.27290 -0.467 0.64051
## category_code_LT01_9_count 0.37280 0.22486 1.658 0.09798 .
## category_code_LT01_11_count 0.35355 0.11540 3.064 0.00231 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6262
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.619034136109423
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0197 -0.7911 0.0119 0.9017 3.4513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.941843 0.088228 112.684 < 2e-16 ***
## category_code_LT01_1_count 0.438616 0.083678 5.242 2.37e-07 ***
## category_code_LT01_2_count 0.816913 0.073474 11.118 < 2e-16 ***
## category_code_LT01_5_count 0.999809 0.062218 16.070 < 2e-16 ***
## category_code_LT01_8_count -0.147104 0.275584 -0.534 0.594
## category_code_LT01_9_count 0.415875 0.226559 1.836 0.067 .
## category_code_LT01_12_count 0.001664 0.208738 0.008 0.994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6236, Adjusted R-squared: 0.619
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.619076309961159
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0194 -0.7913 0.0142 0.8989 3.4512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94200 0.08822 112.691 < 2e-16 ***
## category_code_LT01_1_count 0.43537 0.08409 5.178 3.28e-07 ***
## category_code_LT01_2_count 0.81551 0.07230 11.280 < 2e-16 ***
## category_code_LT01_5_count 0.99902 0.06198 16.117 < 2e-16 ***
## category_code_LT01_8_count -0.14317 0.27590 -0.519 0.6040
## category_code_LT01_9_count 0.41939 0.22705 1.847 0.0653 .
## category_code_LT01_13_count 0.05773 0.24745 0.233 0.8156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619095069690606
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0197 -0.7912 0.0135 0.8972 3.4499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94322 0.08836 112.535 < 2e-16 ***
## category_code_LT01_1_count 0.43552 0.08364 5.207 2.82e-07 ***
## category_code_LT01_2_count 0.81475 0.07246 11.244 < 2e-16 ***
## category_code_LT01_5_count 0.99779 0.06232 16.012 < 2e-16 ***
## category_code_LT01_8_count -0.14785 0.27541 -0.537 0.5916
## category_code_LT01_9_count 0.41183 0.22700 1.814 0.0702 .
## category_code_LT01_14_count 0.09246 0.32977 0.280 0.7793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.619105681933996
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0192 -0.8027 0.0218 0.8959 3.4519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94126 0.08824 112.664 < 2e-16 ***
## category_code_LT01_1_count 0.44365 0.08444 5.254 2.22e-07 ***
## category_code_LT01_2_count 0.81879 0.07223 11.335 < 2e-16 ***
## category_code_LT01_5_count 0.99986 0.06188 16.159 < 2e-16 ***
## category_code_LT01_8_count -0.14640 0.27540 -0.532 0.5952
## category_code_LT01_9_count 0.41367 0.22665 1.825 0.0686 .
## category_code_LT01_15_count -0.23226 0.76452 -0.304 0.7614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.619094101066356
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0200 -0.7905 0.0136 0.9046 3.4510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94211 0.08822 112.691 < 2e-16 ***
## category_code_LT01_1_count 0.43967 0.08293 5.302 1.74e-07 ***
## category_code_LT01_2_count 0.81443 0.07260 11.218 < 2e-16 ***
## category_code_LT01_5_count 0.99975 0.06188 16.156 < 2e-16 ***
## category_code_LT01_8_count -0.15092 0.27575 -0.547 0.5844
## category_code_LT01_9_count 0.41406 0.22664 1.827 0.0683 .
## category_code_LT01_16_count 0.32744 1.17725 0.278 0.7810
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.625334747520155
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9972 -0.7719 0.0460 0.8781 3.4654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92775 0.09090 109.211 < 2e-16 ***
## category_code_LT01_1_count 0.37609 0.08527 4.411 1.27e-05 ***
## category_code_LT01_2_count 0.66927 0.08723 7.672 9.16e-14 ***
## category_code_LT01_5_count 0.98756 0.06150 16.059 < 2e-16 ***
## category_code_LT01_8_count -0.12069 0.27313 -0.442 0.65876
## category_code_LT01_10_count 0.14287 0.11175 1.278 0.20170
## category_code_LT01_11_count 0.35734 0.11548 3.094 0.00209 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6253
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.618030796199801
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9939 -0.7936 0.0121 0.9029 3.4828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91032 0.09162 108.162 < 2e-16 ***
## category_code_LT01_1_count 0.44945 0.08357 5.378 1.17e-07 ***
## category_code_LT01_2_count 0.82668 0.07308 11.311 < 2e-16 ***
## category_code_LT01_5_count 1.00795 0.06208 16.237 < 2e-16 ***
## category_code_LT01_8_count -0.13931 0.27586 -0.505 0.614
## category_code_LT01_10_count 0.16227 0.11276 1.439 0.151
## category_code_LT01_12_count -0.01085 0.20919 -0.052 0.959
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.61803163611245
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9937 -0.7937 0.0117 0.9041 3.4827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91050 0.09163 108.162 < 2e-16 ***
## category_code_LT01_1_count 0.44799 0.08391 5.339 1.43e-07 ***
## category_code_LT01_2_count 0.82565 0.07189 11.485 < 2e-16 ***
## category_code_LT01_5_count 1.00742 0.06183 16.294 < 2e-16 ***
## category_code_LT01_8_count -0.13872 0.27625 -0.502 0.616
## category_code_LT01_10_count 0.16179 0.11273 1.435 0.152
## category_code_LT01_13_count 0.01519 0.24739 0.061 0.951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.618034078131443
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9941 -0.7943 0.0117 0.9050 3.4819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91126 0.09220 107.501 < 2e-16 ***
## category_code_LT01_1_count 0.44785 0.08361 5.357 1.31e-07 ***
## category_code_LT01_2_count 0.82543 0.07199 11.466 < 2e-16 ***
## category_code_LT01_5_count 1.00699 0.06221 16.187 < 2e-16 ***
## category_code_LT01_8_count -0.13996 0.27571 -0.508 0.612
## category_code_LT01_10_count 0.15993 0.11546 1.385 0.167
## category_code_LT01_14_count 0.02808 0.33774 0.083 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.618210069285238
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9917 -0.7887 0.0301 0.9045 3.4847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90846 0.09168 108.079 < 2e-16 ***
## category_code_LT01_1_count 0.45661 0.08429 5.417 9.49e-08 ***
## category_code_LT01_2_count 0.82795 0.07182 11.528 < 2e-16 ***
## category_code_LT01_5_count 1.00746 0.06173 16.320 < 2e-16 ***
## category_code_LT01_8_count -0.13922 0.27564 -0.505 0.614
## category_code_LT01_10_count 0.16656 0.11303 1.474 0.141
## category_code_LT01_15_count -0.37075 0.76768 -0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.618089924690978
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9941 -0.7941 0.0076 0.9052 3.4823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91089 0.09162 108.172 < 2e-16 ***
## category_code_LT01_1_count 0.44977 0.08282 5.431 8.84e-08 ***
## category_code_LT01_2_count 0.82334 0.07232 11.385 < 2e-16 ***
## category_code_LT01_5_count 1.00747 0.06174 16.317 < 2e-16 ***
## category_code_LT01_8_count -0.14373 0.27604 -0.521 0.603
## category_code_LT01_10_count 0.16093 0.11272 1.428 0.154
## category_code_LT01_16_count 0.33078 1.17903 0.281 0.779
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6181
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.624486378435583
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0321 -0.7765 0.0355 0.8874 3.4339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95923 0.08768 113.591 < 2e-16 ***
## category_code_LT01_1_count 0.37989 0.08560 4.438 1.12e-05 ***
## category_code_LT01_2_count 0.68571 0.08667 7.912 1.69e-14 ***
## category_code_LT01_5_count 0.99264 0.06176 16.072 < 2e-16 ***
## category_code_LT01_8_count -0.10344 0.27357 -0.378 0.70550
## category_code_LT01_11_count 0.38472 0.11846 3.248 0.00124 **
## category_code_LT01_12_count -0.15357 0.21267 -0.722 0.47057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.6240876483265
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0293 -0.7485 0.0396 0.8970 3.4342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958912 0.087725 113.525 < 2e-16 ***
## category_code_LT01_1_count 0.375136 0.086393 4.342 1.71e-05 ***
## category_code_LT01_2_count 0.683487 0.086741 7.880 2.13e-14 ***
## category_code_LT01_5_count 0.988960 0.061661 16.039 < 2e-16 ***
## category_code_LT01_8_count -0.111322 0.274003 -0.406 0.68471
## category_code_LT01_11_count 0.365484 0.115559 3.163 0.00166 **
## category_code_LT01_13_count 0.002171 0.245402 0.009 0.99294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.624176001626739
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0291 -0.7634 0.0456 0.8900 3.4327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96050 0.08784 113.398 < 2e-16 ***
## category_code_LT01_1_count 0.37144 0.08613 4.312 1.95e-05 ***
## category_code_LT01_2_count 0.68076 0.08703 7.822 3.20e-14 ***
## category_code_LT01_5_count 0.98643 0.06204 15.900 < 2e-16 ***
## category_code_LT01_8_count -0.11270 0.27348 -0.412 0.68046
## category_code_LT01_11_count 0.36478 0.11550 3.158 0.00169 **
## category_code_LT01_14_count 0.11112 0.32695 0.340 0.73410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.62422937970836
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0284 -0.7477 0.0400 0.8945 3.4351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95810 0.08773 113.515 < 2e-16 ***
## category_code_LT01_1_count 0.38191 0.08678 4.401 1.32e-05 ***
## category_code_LT01_2_count 0.68532 0.08675 7.900 1.84e-14 ***
## category_code_LT01_5_count 0.98885 0.06158 16.059 < 2e-16 ***
## category_code_LT01_8_count -0.11067 0.27344 -0.405 0.6859
## category_code_LT01_11_count 0.36655 0.11550 3.174 0.0016 **
## category_code_LT01_15_count -0.32676 0.75914 -0.430 0.6671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6288, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.624184600558836
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0295 -0.7488 0.0414 0.8994 3.4339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95924 0.08772 113.540 < 2e-16 ***
## category_code_LT01_1_count 0.37635 0.08545 4.404 1.3e-05 ***
## category_code_LT01_2_count 0.67993 0.08724 7.794 3.9e-14 ***
## category_code_LT01_5_count 0.98877 0.06158 16.056 < 2e-16 ***
## category_code_LT01_8_count -0.11650 0.27382 -0.425 0.67069
## category_code_LT01_11_count 0.36581 0.11549 3.168 0.00163 **
## category_code_LT01_16_count 0.41615 1.16891 0.356 0.72198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616429299756156
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7930 0.0003 0.9047 3.4477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.945487 0.088511 112.364 < 2e-16 ***
## category_code_LT01_1_count 0.448179 0.084872 5.281 1.94e-07 ***
## category_code_LT01_2_count 0.845549 0.072168 11.716 < 2e-16 ***
## category_code_LT01_5_count 1.009364 0.062276 16.208 < 2e-16 ***
## category_code_LT01_8_count -0.127955 0.276904 -0.462 0.644
## category_code_LT01_12_count 0.001257 0.209462 0.006 0.995
## category_code_LT01_13_count 0.027374 0.247773 0.110 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616541835737331
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7945 0.0017 0.8885 3.4459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.947288 0.088624 112.242 < 2e-16 ***
## category_code_LT01_1_count 0.445320 0.084463 5.272 2.02e-07 ***
## category_code_LT01_2_count 0.842862 0.072437 11.636 < 2e-16 ***
## category_code_LT01_5_count 1.006810 0.062618 16.079 < 2e-16 ***
## category_code_LT01_8_count -0.131028 0.276339 -0.474 0.636
## category_code_LT01_12_count -0.003582 0.209815 -0.017 0.986
## category_code_LT01_14_count 0.130793 0.330824 0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616521650935027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0295 -0.7968 0.0100 0.8991 3.4485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.944673 0.088519 112.345 < 2e-16 ***
## category_code_LT01_1_count 0.455715 0.085373 5.338 1.44e-07 ***
## category_code_LT01_2_count 0.848280 0.072209 11.748 < 2e-16 ***
## category_code_LT01_5_count 1.009747 0.062187 16.237 < 2e-16 ***
## category_code_LT01_8_count -0.129074 0.276340 -0.467 0.641
## category_code_LT01_12_count -0.001776 0.209622 -0.008 0.993
## category_code_LT01_15_count -0.277187 0.767442 -0.361 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.616504781041981
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0303 -0.7932 -0.0018 0.9060 3.4475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.945704 0.088504 112.376 < 2e-16 ***
## category_code_LT01_1_count 0.450727 0.083794 5.379 1.16e-07 ***
## category_code_LT01_2_count 0.842817 0.072666 11.599 < 2e-16 ***
## category_code_LT01_5_count 1.009493 0.062192 16.232 < 2e-16 ***
## category_code_LT01_8_count -0.134635 0.276715 -0.487 0.627
## category_code_LT01_12_count 0.002621 0.209457 0.013 0.990
## category_code_LT01_16_count 0.389609 1.180912 0.330 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616552013359692
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0298 -0.7946 0.0024 0.8902 3.4458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94740 0.08863 112.239 < 2e-16 ***
## category_code_LT01_1_count 0.44351 0.08492 5.223 2.61e-07 ***
## category_code_LT01_2_count 0.84198 0.07127 11.815 < 2e-16 ***
## category_code_LT01_5_count 1.00632 0.06242 16.122 < 2e-16 ***
## category_code_LT01_8_count -0.12921 0.27670 -0.467 0.641
## category_code_LT01_13_count 0.02860 0.24774 0.115 0.908
## category_code_LT01_14_count 0.13091 0.33022 0.396 0.692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616526401513362
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0293 -0.7940 0.0094 0.9019 3.4484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94476 0.08852 112.342 < 2e-16 ***
## category_code_LT01_1_count 0.45440 0.08587 5.292 1.83e-07 ***
## category_code_LT01_2_count 0.84769 0.07091 11.955 < 2e-16 ***
## category_code_LT01_5_count 1.00944 0.06193 16.298 < 2e-16 ***
## category_code_LT01_8_count -0.12782 0.27669 -0.462 0.644
## category_code_LT01_13_count 0.01951 0.24873 0.078 0.938
## category_code_LT01_15_count -0.27148 0.76982 -0.353 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.616516242590618
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0302 -0.7933 -0.0096 0.9065 3.4474
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94580 0.08850 112.376 < 2e-16 ***
## category_code_LT01_1_count 0.44919 0.08413 5.340 1.43e-07 ***
## category_code_LT01_2_count 0.84230 0.07137 11.801 < 2e-16 ***
## category_code_LT01_5_count 1.00918 0.06194 16.293 < 2e-16 ***
## category_code_LT01_8_count -0.13248 0.27704 -0.478 0.633
## category_code_LT01_13_count 0.03019 0.24787 0.122 0.903
## category_code_LT01_16_count 0.39424 1.18142 0.334 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.61664189664465
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0292 -0.7938 0.0156 0.8871 3.4466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94657 0.08863 112.220 < 2e-16 ***
## category_code_LT01_1_count 0.45094 0.08529 5.287 1.87e-07 ***
## category_code_LT01_2_count 0.84454 0.07124 11.855 < 2e-16 ***
## category_code_LT01_5_count 1.00666 0.06232 16.153 < 2e-16 ***
## category_code_LT01_8_count -0.13055 0.27615 -0.473 0.637
## category_code_LT01_14_count 0.12960 0.33017 0.393 0.695
## category_code_LT01_15_count -0.27475 0.76662 -0.358 0.720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616638413814283
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0301 -0.7905 -0.0093 0.8954 3.4454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94771 0.08862 112.251 < 2e-16 ***
## category_code_LT01_1_count 0.44607 0.08378 5.324 1.55e-07 ***
## category_code_LT01_2_count 0.83898 0.07179 11.687 < 2e-16 ***
## category_code_LT01_5_count 1.00636 0.06233 16.146 < 2e-16 ***
## category_code_LT01_8_count -0.13630 0.27652 -0.493 0.622
## category_code_LT01_14_count 0.13686 0.33066 0.414 0.679
## category_code_LT01_16_count 0.41632 1.18235 0.352 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616600798501361
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0296 -0.7938 0.0073 0.8986 3.4482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94499 0.08851 112.356 < 2e-16 ***
## category_code_LT01_1_count 0.45650 0.08451 5.402 1.03e-07 ***
## category_code_LT01_2_count 0.84497 0.07135 11.842 < 2e-16 ***
## category_code_LT01_5_count 1.00951 0.06185 16.323 < 2e-16 ***
## category_code_LT01_8_count -0.13373 0.27651 -0.484 0.629
## category_code_LT01_15_count -0.26914 0.76703 -0.351 0.726
## category_code_LT01_16_count 0.37620 1.18121 0.318 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.626902146849334
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9890 -0.7662 0.0782 0.8804 3.4652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92794 0.09070 109.460 < 2e-16 ***
## category_code_LT01_1_count 0.36730 0.08514 4.314 1.94e-05 ***
## category_code_LT01_2_count 0.65194 0.08778 7.427 4.96e-13 ***
## category_code_LT01_5_count 0.97549 0.06080 16.043 < 2e-16 ***
## category_code_LT01_9_count 0.33985 0.22613 1.503 0.13350
## category_code_LT01_10_count 0.12140 0.11229 1.081 0.28016
## category_code_LT01_11_count 0.34886 0.11539 3.023 0.00263 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.61995961326624
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9847 -0.7767 0.0384 0.9204 3.4822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91094 0.09138 108.462 < 2e-16 ***
## category_code_LT01_1_count 0.43782 0.08354 5.241 2.38e-07 ***
## category_code_LT01_2_count 0.80343 0.07429 10.815 < 2e-16 ***
## category_code_LT01_5_count 0.99390 0.06144 16.176 < 2e-16 ***
## category_code_LT01_9_count 0.37776 0.22787 1.658 0.098 .
## category_code_LT01_10_count 0.13789 0.11330 1.217 0.224
## category_code_LT01_12_count -0.01291 0.20854 -0.062 0.951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.619992394347087
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9845 -0.7790 0.0422 0.9222 3.4817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91143 0.09138 108.464 < 2e-16 ***
## category_code_LT01_1_count 0.43406 0.08390 5.174 3.35e-07 ***
## category_code_LT01_2_count 0.80126 0.07319 10.948 < 2e-16 ***
## category_code_LT01_5_count 0.99285 0.06114 16.238 < 2e-16 ***
## category_code_LT01_9_count 0.38140 0.22847 1.669 0.0957 .
## category_code_LT01_10_count 0.13659 0.11329 1.206 0.2285
## category_code_LT01_13_count 0.05307 0.24692 0.215 0.8299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.619956846703851
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9846 -0.7872 0.0380 0.9234 3.4820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.911196 0.091945 107.795 < 2e-16 ***
## category_code_LT01_1_count 0.436902 0.083528 5.231 2.51e-07 ***
## category_code_LT01_2_count 0.802465 0.073164 10.968 < 2e-16 ***
## category_code_LT01_5_count 0.993365 0.061521 16.147 < 2e-16 ***
## category_code_LT01_9_count 0.377683 0.228036 1.656 0.0983 .
## category_code_LT01_10_count 0.137199 0.115862 1.184 0.2369
## category_code_LT01_14_count 0.005433 0.337130 0.016 0.9871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.62009080312559
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9829 -0.7910 0.0453 0.9122 3.4838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90935 0.09144 108.372 < 2e-16 ***
## category_code_LT01_1_count 0.44389 0.08429 5.266 2.09e-07 ***
## category_code_LT01_2_count 0.80452 0.07309 11.008 < 2e-16 ***
## category_code_LT01_5_count 0.99347 0.06106 16.270 < 2e-16 ***
## category_code_LT01_9_count 0.37380 0.22803 1.639 0.102
## category_code_LT01_10_count 0.14175 0.11361 1.248 0.213
## category_code_LT01_15_count -0.31915 0.76647 -0.416 0.677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6247, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.619991263829676
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9848 -0.7868 0.0403 0.9225 3.4818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91138 0.09138 108.468 < 2e-16 ***
## category_code_LT01_1_count 0.43779 0.08277 5.289 1.85e-07 ***
## category_code_LT01_2_count 0.80068 0.07348 10.896 < 2e-16 ***
## category_code_LT01_5_count 0.99330 0.06108 16.263 < 2e-16 ***
## category_code_LT01_9_count 0.37656 0.22794 1.652 0.0992 .
## category_code_LT01_10_count 0.13682 0.11325 1.208 0.2276
## category_code_LT01_16_count 0.24850 1.17498 0.211 0.8326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626406241003075
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0189 -0.7748 0.0606 0.8634 3.4386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95460 0.08745 113.833 < 2e-16 ***
## category_code_LT01_1_count 0.37084 0.08544 4.340 1.73e-05 ***
## category_code_LT01_2_count 0.66475 0.08736 7.609 1.42e-13 ***
## category_code_LT01_5_count 0.98025 0.06109 16.046 < 2e-16 ***
## category_code_LT01_9_count 0.36691 0.22467 1.633 0.10310
## category_code_LT01_11_count 0.37385 0.11834 3.159 0.00168 **
## category_code_LT01_12_count -0.15220 0.21197 -0.718 0.47308
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626031131184183
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0156 -0.7897 0.0682 0.8506 3.4389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95429 0.08749 113.772 < 2e-16 ***
## category_code_LT01_1_count 0.36412 0.08623 4.223 2.88e-05 ***
## category_code_LT01_2_count 0.66175 0.08747 7.566 1.92e-13 ***
## category_code_LT01_5_count 0.97588 0.06093 16.016 < 2e-16 ***
## category_code_LT01_9_count 0.37146 0.22533 1.649 0.09989 .
## category_code_LT01_11_count 0.35415 0.11548 3.067 0.00228 **
## category_code_LT01_13_count 0.03679 0.24492 0.150 0.88066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626055117334288
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0157 -0.7904 0.0706 0.8504 3.4379
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95530 0.08762 113.620 < 2e-16 ***
## category_code_LT01_1_count 0.36354 0.08593 4.231 2.78e-05 ***
## category_code_LT01_2_count 0.66073 0.08765 7.538 2.32e-13 ***
## category_code_LT01_5_count 0.97459 0.06131 15.897 < 2e-16 ***
## category_code_LT01_9_count 0.36578 0.22521 1.624 0.10498
## category_code_LT01_11_count 0.35442 0.11540 3.071 0.00225 **
## category_code_LT01_14_count 0.07597 0.32675 0.233 0.81624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.626123992565741
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7889 0.0666 0.8460 3.4396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95351 0.08750 113.757 < 2e-16 ***
## category_code_LT01_1_count 0.37203 0.08664 4.294 2.12e-05 ***
## category_code_LT01_2_count 0.66418 0.08746 7.594 1.57e-13 ***
## category_code_LT01_5_count 0.97626 0.06086 16.040 < 2e-16 ***
## category_code_LT01_9_count 0.36624 0.22486 1.629 0.10401
## category_code_LT01_11_count 0.35580 0.11541 3.083 0.00216 **
## category_code_LT01_15_count -0.28801 0.75762 -0.380 0.70399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.62607557403253
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0158 -0.7898 0.0698 0.8501 3.4387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95444 0.08749 113.778 < 2e-16 ***
## category_code_LT01_1_count 0.36695 0.08528 4.303 2.04e-05 ***
## category_code_LT01_2_count 0.65967 0.08788 7.506 2.88e-13 ***
## category_code_LT01_5_count 0.97602 0.06087 16.033 < 2e-16 ***
## category_code_LT01_9_count 0.36708 0.22486 1.632 0.1032
## category_code_LT01_11_count 0.35515 0.11539 3.078 0.0022 **
## category_code_LT01_16_count 0.33140 1.16497 0.284 0.7762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.618867518353111
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.8088 0.0210 0.9074 3.4524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.940744 0.088216 112.686 < 2e-16 ***
## category_code_LT01_1_count 0.433490 0.084815 5.111 4.6e-07 ***
## category_code_LT01_2_count 0.815856 0.073759 11.061 < 2e-16 ***
## category_code_LT01_5_count 0.994037 0.061597 16.138 < 2e-16 ***
## category_code_LT01_9_count 0.415862 0.227008 1.832 0.0676 .
## category_code_LT01_12_count -0.002701 0.208664 -0.013 0.9897
## category_code_LT01_13_count 0.065446 0.247081 0.265 0.7912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.618872095580285
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0150 -0.8076 0.0215 0.9094 3.4513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.941866 0.088347 112.532 < 2e-16 ***
## category_code_LT01_1_count 0.434242 0.084336 5.149 3.8e-07 ***
## category_code_LT01_2_count 0.815557 0.073836 11.046 < 2e-16 ***
## category_code_LT01_5_count 0.992871 0.061931 16.032 < 2e-16 ***
## category_code_LT01_9_count 0.407750 0.226940 1.797 0.073 .
## category_code_LT01_12_count -0.005785 0.209055 -0.028 0.978
## category_code_LT01_14_count 0.091140 0.330475 0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.618886902525529
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0144 -0.8082 0.0329 0.9060 3.4532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.939930 0.088230 112.659 < 2e-16 ***
## category_code_LT01_1_count 0.442384 0.085326 5.185 3.17e-07 ***
## category_code_LT01_2_count 0.819536 0.073758 11.111 < 2e-16 ***
## category_code_LT01_5_count 0.994928 0.061525 16.171 < 2e-16 ***
## category_code_LT01_9_count 0.409534 0.226586 1.807 0.0713 .
## category_code_LT01_12_count -0.004998 0.208845 -0.024 0.9809
## category_code_LT01_15_count -0.236092 0.765433 -0.308 0.7579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.618861761723222
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0150 -0.8033 0.0278 0.9166 3.4524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.940735 0.088217 112.685 < 2e-16 ***
## category_code_LT01_1_count 0.437980 0.083713 5.232 2.49e-07 ***
## category_code_LT01_2_count 0.815176 0.074092 11.002 < 2e-16 ***
## category_code_LT01_5_count 0.994573 0.061534 16.163 < 2e-16 ***
## category_code_LT01_9_count 0.410022 0.226584 1.810 0.071 .
## category_code_LT01_12_count -0.001472 0.208679 -0.007 0.994
## category_code_LT01_16_count 0.294624 1.176217 0.250 0.802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.618926912877554
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0145 -0.8044 0.0284 0.9110 3.4510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94211 0.08834 112.538 < 2e-16 ***
## category_code_LT01_1_count 0.43015 0.08480 5.073 5.58e-07 ***
## category_code_LT01_2_count 0.81340 0.07277 11.177 < 2e-16 ***
## category_code_LT01_5_count 0.99187 0.06168 16.081 < 2e-16 ***
## category_code_LT01_9_count 0.41188 0.22744 1.811 0.0708 .
## category_code_LT01_13_count 0.06602 0.24706 0.267 0.7894
## category_code_LT01_14_count 0.09134 0.32983 0.277 0.7819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.618930185396117
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0141 -0.8082 0.0289 0.9067 3.4530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94019 0.08823 112.662 < 2e-16 ***
## category_code_LT01_1_count 0.43837 0.08585 5.106 4.71e-07 ***
## category_code_LT01_2_count 0.81749 0.07259 11.261 < 2e-16 ***
## category_code_LT01_5_count 0.99404 0.06122 16.237 < 2e-16 ***
## category_code_LT01_9_count 0.41339 0.22715 1.820 0.0694 .
## category_code_LT01_13_count 0.05889 0.24811 0.237 0.8125
## category_code_LT01_15_count -0.21845 0.76797 -0.284 0.7762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.618919870646164
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.8064 0.0235 0.9091 3.4522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94098 0.08821 112.692 < 2e-16 ***
## category_code_LT01_1_count 0.43407 0.08406 5.164 3.52e-07 ***
## category_code_LT01_2_count 0.81319 0.07294 11.150 < 2e-16 ***
## category_code_LT01_5_count 0.99369 0.06123 16.229 < 2e-16 ***
## category_code_LT01_9_count 0.41420 0.22708 1.824 0.0688 .
## category_code_LT01_13_count 0.06766 0.24721 0.274 0.7844
## category_code_LT01_16_count 0.30599 1.17672 0.260 0.7949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.618944330006932
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0143 -0.8090 0.0292 0.9104 3.4519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94128 0.08836 112.510 < 2e-16 ***
## category_code_LT01_1_count 0.43895 0.08519 5.153 3.73e-07 ***
## category_code_LT01_2_count 0.81695 0.07270 11.237 < 2e-16 ***
## category_code_LT01_5_count 0.99272 0.06160 16.116 < 2e-16 ***
## category_code_LT01_9_count 0.40559 0.22703 1.786 0.0746 .
## category_code_LT01_14_count 0.09006 0.32982 0.273 0.7849
## category_code_LT01_15_count -0.23425 0.76467 -0.306 0.7595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.618926644918049
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0149 -0.8054 0.0264 0.9098 3.4510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94217 0.08835 112.535 < 2e-16 ***
## category_code_LT01_1_count 0.43465 0.08365 5.196 2.99e-07 ***
## category_code_LT01_2_count 0.81256 0.07313 11.112 < 2e-16 ***
## category_code_LT01_5_count 0.99234 0.06162 16.105 < 2e-16 ***
## category_code_LT01_9_count 0.40571 0.22706 1.787 0.0746 .
## category_code_LT01_14_count 0.09554 0.33035 0.289 0.7725
## category_code_LT01_16_count 0.31397 1.17787 0.267 0.7899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.618931673159978
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0144 -0.8052 0.0319 0.9114 3.4530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94016 0.08823 112.664 < 2e-16 ***
## category_code_LT01_1_count 0.44276 0.08446 5.243 2.36e-07 ***
## category_code_LT01_2_count 0.81690 0.07286 11.211 < 2e-16 ***
## category_code_LT01_5_count 0.99455 0.06115 16.263 < 2e-16 ***
## category_code_LT01_9_count 0.40792 0.22667 1.800 0.0725 .
## category_code_LT01_15_count -0.22967 0.76503 -0.300 0.7641
## category_code_LT01_16_count 0.28398 1.17653 0.241 0.8094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.625647465336799
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9961 -0.7844 0.0572 0.8817 3.4664
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92680 0.09085 109.266 < 2e-16 ***
## category_code_LT01_1_count 0.37954 0.08540 4.444 1.09e-05 ***
## category_code_LT01_2_count 0.67111 0.08724 7.693 7.93e-14 ***
## category_code_LT01_5_count 0.98744 0.06093 16.207 < 2e-16 ***
## category_code_LT01_10_count 0.14421 0.11172 1.291 0.19736
## category_code_LT01_11_count 0.37892 0.11835 3.202 0.00146 **
## category_code_LT01_12_count -0.16518 0.21226 -0.778 0.43682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.625185761375972
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9934 -0.7766 0.0401 0.8889 3.4662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.926954 0.090919 109.184 < 2e-16 ***
## category_code_LT01_1_count 0.374493 0.086153 4.347 1.68e-05 ***
## category_code_LT01_2_count 0.669004 0.087310 7.662 9.82e-14 ***
## category_code_LT01_5_count 0.983248 0.060769 16.180 < 2e-16 ***
## category_code_LT01_10_count 0.141584 0.111790 1.267 0.20593
## category_code_LT01_11_count 0.358511 0.115536 3.103 0.00203 **
## category_code_LT01_13_count -0.001292 0.244699 -0.005 0.99579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6297, Adjusted R-squared: 0.6252
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.625188454368795
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9937 -0.7766 0.0422 0.8912 3.4656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92757 0.09148 108.523 < 2e-16 ***
## category_code_LT01_1_count 0.37373 0.08600 4.346 1.69e-05 ***
## category_code_LT01_2_count 0.66864 0.08744 7.647 1.09e-13 ***
## category_code_LT01_5_count 0.98279 0.06119 16.061 < 2e-16 ***
## category_code_LT01_10_count 0.14007 0.11450 1.223 0.22179
## category_code_LT01_11_count 0.35845 0.11548 3.104 0.00202 **
## category_code_LT01_14_count 0.01995 0.33456 0.060 0.95247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6297, Adjusted R-squared: 0.6252
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.625408017895637
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9911 -0.7661 0.0415 0.8916 3.4683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92488 0.09096 109.112 < 2e-16 ***
## category_code_LT01_1_count 0.38283 0.08659 4.421 1.21e-05 ***
## category_code_LT01_2_count 0.67076 0.08728 7.685 8.40e-14 ***
## category_code_LT01_5_count 0.98304 0.06071 16.193 < 2e-16 ***
## category_code_LT01_10_count 0.14653 0.11208 1.307 0.19170
## category_code_LT01_11_count 0.35949 0.11546 3.114 0.00196 **
## category_code_LT01_15_count -0.41049 0.76050 -0.540 0.58960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.625249422337331
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9937 -0.7766 0.0448 0.8931 3.4657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92746 0.09091 109.196 < 2e-16 ***
## category_code_LT01_1_count 0.37526 0.08524 4.402 1.32e-05 ***
## category_code_LT01_2_count 0.66618 0.08778 7.589 1.63e-13 ***
## category_code_LT01_5_count 0.98293 0.06073 16.186 < 2e-16 ***
## category_code_LT01_10_count 0.14038 0.11180 1.256 0.210
## category_code_LT01_11_count 0.35884 0.11547 3.108 0.002 **
## category_code_LT01_16_count 0.33694 1.16648 0.289 0.773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6252
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617839248363076
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9895 -0.7914 0.0261 0.9014 3.4837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90949 0.09164 108.131 < 2e-16 ***
## category_code_LT01_1_count 0.44673 0.08463 5.278 1.96e-07 ***
## category_code_LT01_2_count 0.82672 0.07326 11.285 < 2e-16 ***
## category_code_LT01_5_count 1.00291 0.06141 16.331 < 2e-16 ***
## category_code_LT01_10_count 0.16057 0.11282 1.423 0.155
## category_code_LT01_12_count -0.01457 0.20913 -0.070 0.944
## category_code_LT01_13_count 0.02314 0.24697 0.094 0.925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617837802728887
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9900 -0.8013 0.0277 0.9041 3.4830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91018 0.09220 107.481 < 2e-16 ***
## category_code_LT01_1_count 0.44706 0.08430 5.303 1.73e-07 ***
## category_code_LT01_2_count 0.82672 0.07330 11.279 < 2e-16 ***
## category_code_LT01_5_count 1.00254 0.06179 16.225 < 2e-16 ***
## category_code_LT01_10_count 0.15883 0.11551 1.375 0.170
## category_code_LT01_12_count -0.01537 0.20943 -0.073 0.942
## category_code_LT01_14_count 0.02814 0.33831 0.083 0.934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.618018284096728
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7893 0.0393 0.9001 3.4858
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90733 0.09170 108.047 < 2e-16 ***
## category_code_LT01_1_count 0.45615 0.08517 5.356 1.31e-07 ***
## category_code_LT01_2_count 0.82952 0.07324 11.327 < 2e-16 ***
## category_code_LT01_5_count 1.00316 0.06134 16.353 < 2e-16 ***
## category_code_LT01_10_count 0.16562 0.11314 1.464 0.144
## category_code_LT01_12_count -0.01922 0.20930 -0.092 0.927
## category_code_LT01_15_count -0.37573 0.76870 -0.489 0.625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617882337585894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7922 0.0238 0.9016 3.4834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90975 0.09164 108.142 < 2e-16 ***
## category_code_LT01_1_count 0.44875 0.08359 5.368 1.23e-07 ***
## category_code_LT01_2_count 0.82477 0.07371 11.190 < 2e-16 ***
## category_code_LT01_5_count 1.00286 0.06137 16.342 < 2e-16 ***
## category_code_LT01_10_count 0.15986 0.11283 1.417 0.157
## category_code_LT01_12_count -0.01359 0.20914 -0.065 0.948
## category_code_LT01_16_count 0.29834 1.17797 0.253 0.800
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617840611299012
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7927 0.0234 0.8992 3.4827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91044 0.09222 107.464 < 2e-16 ***
## category_code_LT01_1_count 0.44493 0.08472 5.252 2.25e-07 ***
## category_code_LT01_2_count 0.82523 0.07219 11.432 < 2e-16 ***
## category_code_LT01_5_count 1.00181 0.06153 16.283 < 2e-16 ***
## category_code_LT01_10_count 0.15818 0.11554 1.369 0.172
## category_code_LT01_13_count 0.02343 0.24701 0.095 0.924
## category_code_LT01_14_count 0.02747 0.33789 0.081 0.935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.618013535696687
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9873 -0.7894 0.0362 0.8939 3.4856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90759 0.09171 108.036 < 2e-16 ***
## category_code_LT01_1_count 0.45427 0.08561 5.306 1.7e-07 ***
## category_code_LT01_2_count 0.82795 0.07204 11.493 < 2e-16 ***
## category_code_LT01_5_count 1.00242 0.06103 16.425 < 2e-16 ***
## category_code_LT01_10_count 0.16492 0.11311 1.458 0.145
## category_code_LT01_13_count 0.01196 0.24798 0.048 0.962
## category_code_LT01_15_count -0.36896 0.77122 -0.478 0.633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617887299593384
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9896 -0.7918 0.0244 0.8977 3.4831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91004 0.09164 108.138 < 2e-16 ***
## category_code_LT01_1_count 0.44660 0.08387 5.325 1.54e-07 ***
## category_code_LT01_2_count 0.82330 0.07253 11.352 < 2e-16 ***
## category_code_LT01_5_count 1.00215 0.06105 16.415 < 2e-16 ***
## category_code_LT01_10_count 0.15916 0.11279 1.411 0.159
## category_code_LT01_13_count 0.02544 0.24712 0.103 0.918
## category_code_LT01_16_count 0.30418 1.17867 0.258 0.796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.61801519312512
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7899 0.0381 0.8948 3.4850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90818 0.09228 107.375 < 2e-16 ***
## category_code_LT01_1_count 0.45418 0.08515 5.334 1.47e-07 ***
## category_code_LT01_2_count 0.82779 0.07212 11.479 < 2e-16 ***
## category_code_LT01_5_count 1.00204 0.06145 16.306 < 2e-16 ***
## category_code_LT01_10_count 0.16344 0.11587 1.411 0.159
## category_code_LT01_14_count 0.02256 0.33786 0.067 0.947
## category_code_LT01_15_count -0.37110 0.76812 -0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617886187665634
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9900 -0.7942 0.0246 0.9006 3.4823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.91085 0.09222 107.466 < 2e-16 ***
## category_code_LT01_1_count 0.44686 0.08360 5.345 1.38e-07 ***
## category_code_LT01_2_count 0.82320 0.07265 11.331 < 2e-16 ***
## category_code_LT01_5_count 1.00168 0.06148 16.293 < 2e-16 ***
## category_code_LT01_10_count 0.15710 0.11559 1.359 0.175
## category_code_LT01_14_count 0.03242 0.33849 0.096 0.924
## category_code_LT01_16_count 0.30677 1.18022 0.260 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.618055701504614
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9875 -0.7897 0.0339 0.8941 3.4852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.90792 0.09169 108.055 < 2e-16 ***
## category_code_LT01_1_count 0.45562 0.08428 5.406 1.01e-07 ***
## category_code_LT01_2_count 0.82597 0.07245 11.400 < 2e-16 ***
## category_code_LT01_5_count 1.00231 0.06098 16.436 < 2e-16 ***
## category_code_LT01_10_count 0.16410 0.11310 1.451 0.147
## category_code_LT01_15_count -0.36612 0.76828 -0.477 0.634
## category_code_LT01_16_count 0.28015 1.17826 0.238 0.812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6181
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.62437795243157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.7812 0.0405 0.8688 3.4348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958346 0.087660 113.602 < 2e-16 ***
## category_code_LT01_1_count 0.378126 0.086489 4.372 1.50e-05 ***
## category_code_LT01_2_count 0.685277 0.086754 7.899 1.86e-14 ***
## category_code_LT01_5_count 0.988918 0.061065 16.195 < 2e-16 ***
## category_code_LT01_11_count 0.385922 0.118489 3.257 0.0012 **
## category_code_LT01_12_count -0.156846 0.212524 -0.738 0.4609
## category_code_LT01_13_count 0.008492 0.244845 0.035 0.9723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.624486836558966
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0286 -0.7957 0.0479 0.8664 3.4331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96009 0.08777 113.481 < 2e-16 ***
## category_code_LT01_1_count 0.37444 0.08622 4.343 1.71e-05 ***
## category_code_LT01_2_count 0.68239 0.08703 7.841 2.80e-14 ***
## category_code_LT01_5_count 0.98622 0.06145 16.049 < 2e-16 ***
## category_code_LT01_11_count 0.38584 0.11841 3.259 0.0012 **
## category_code_LT01_12_count -0.16161 0.21287 -0.759 0.4481
## category_code_LT01_14_count 0.12404 0.32736 0.379 0.7049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.624546017165167
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0279 -0.7738 0.0375 0.8540 3.4357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95746 0.08766 113.598 < 2e-16 ***
## category_code_LT01_1_count 0.38601 0.08698 4.438 1.12e-05 ***
## category_code_LT01_2_count 0.68745 0.08677 7.923 1.57e-14 ***
## category_code_LT01_5_count 0.98901 0.06100 16.212 < 2e-16 ***
## category_code_LT01_11_count 0.38778 0.11846 3.274 0.00114 **
## category_code_LT01_12_count -0.16173 0.21273 -0.760 0.44746
## category_code_LT01_15_count -0.35714 0.75971 -0.470 0.63850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.624457947397817
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.7773 0.0445 0.8672 3.4346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95858 0.08765 113.617 < 2e-16 ***
## category_code_LT01_1_count 0.37948 0.08558 4.434 1.14e-05 ***
## category_code_LT01_2_count 0.68210 0.08726 7.817 3.33e-14 ***
## category_code_LT01_5_count 0.98862 0.06102 16.201 < 2e-16 ***
## category_code_LT01_11_count 0.38626 0.11841 3.262 0.00118 **
## category_code_LT01_12_count -0.15596 0.21252 -0.734 0.46339
## category_code_LT01_16_count 0.37958 1.16702 0.325 0.74513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.624047153718606
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0253 -0.7726 0.0443 0.8892 3.4336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.959509 0.087821 113.407 < 2e-16 ***
## category_code_LT01_1_count 0.369457 0.087035 4.245 2.62e-05 ***
## category_code_LT01_2_count 0.680276 0.087122 7.808 3.53e-14 ***
## category_code_LT01_5_count 0.982321 0.061332 16.016 < 2e-16 ***
## category_code_LT01_11_count 0.365651 0.115565 3.164 0.00165 **
## category_code_LT01_13_count 0.009414 0.244976 0.038 0.96936
## category_code_LT01_14_count 0.109517 0.327013 0.335 0.73784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.624104048152819
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0247 -0.7467 0.0450 0.8698 3.4360
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.957111 0.087712 113.521 < 2e-16 ***
## category_code_LT01_1_count 0.380517 0.087880 4.330 1.81e-05 ***
## category_code_LT01_2_count 0.684974 0.086850 7.887 2.03e-14 ***
## category_code_LT01_5_count 0.984879 0.060843 16.187 < 2e-16 ***
## category_code_LT01_11_count 0.367565 0.115568 3.181 0.00156 **
## category_code_LT01_13_count -0.001351 0.245950 -0.005 0.99562
## category_code_LT01_15_count -0.329237 0.762395 -0.432 0.66604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.624047679664812
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7604 0.0477 0.8905 3.4349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95823 0.08770 113.547 < 2e-16 ***
## category_code_LT01_1_count 0.37411 0.08631 4.334 1.77e-05 ***
## category_code_LT01_2_count 0.67956 0.08734 7.780 4.30e-14 ***
## category_code_LT01_5_count 0.98449 0.06086 16.176 < 2e-16 ***
## category_code_LT01_11_count 0.36666 0.11555 3.173 0.0016 **
## category_code_LT01_13_count 0.01131 0.24512 0.046 0.9632
## category_code_LT01_16_count 0.39248 1.16835 0.336 0.7371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.624188010258183
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0244 -0.7612 0.0361 0.8768 3.4345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95866 0.08782 113.398 < 2e-16 ***
## category_code_LT01_1_count 0.37667 0.08746 4.307 2.00e-05 ***
## category_code_LT01_2_count 0.68225 0.08713 7.831 3.02e-14 ***
## category_code_LT01_5_count 0.98233 0.06127 16.033 < 2e-16 ***
## category_code_LT01_11_count 0.36683 0.11550 3.176 0.00159 **
## category_code_LT01_14_count 0.10830 0.32693 0.331 0.74060
## category_code_LT01_15_count -0.32698 0.75918 -0.431 0.66687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.624141488583504
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0253 -0.7730 0.0401 0.8881 3.4333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95985 0.08781 113.422 < 2e-16 ***
## category_code_LT01_1_count 0.37076 0.08609 4.307 2.00e-05 ***
## category_code_LT01_2_count 0.67668 0.08767 7.719 6.64e-14 ***
## category_code_LT01_5_count 0.98188 0.06129 16.019 < 2e-16 ***
## category_code_LT01_11_count 0.36610 0.11549 3.170 0.00162 **
## category_code_LT01_14_count 0.11561 0.32742 0.353 0.72417
## category_code_LT01_16_count 0.41287 1.16914 0.353 0.72413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6241
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.624182961363418
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0247 -0.7470 0.0475 0.8767 3.4358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95740 0.08770 113.538 < 2e-16 ***
## category_code_LT01_1_count 0.38121 0.08675 4.395 1.36e-05 ***
## category_code_LT01_2_count 0.68166 0.08735 7.804 3.65e-14 ***
## category_code_LT01_5_count 0.98451 0.06080 16.192 < 2e-16 ***
## category_code_LT01_11_count 0.36783 0.11549 3.185 0.00154 **
## category_code_LT01_15_count -0.32123 0.75954 -0.423 0.67253
## category_code_LT01_16_count 0.37506 1.16792 0.321 0.74824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616382656514628
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0256 -0.7940 0.0064 0.8917 3.4470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.946205 0.088610 112.247 < 2e-16 ***
## category_code_LT01_1_count 0.442022 0.085544 5.167 3.46e-07 ***
## category_code_LT01_2_count 0.842429 0.072652 11.595 < 2e-16 ***
## category_code_LT01_5_count 1.001926 0.061966 16.169 < 2e-16 ***
## category_code_LT01_12_count -0.007266 0.209745 -0.035 0.972
## category_code_LT01_13_count 0.035837 0.247329 0.145 0.885
## category_code_LT01_14_count 0.129774 0.330905 0.392 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616360248482971
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0251 -0.7987 0.0157 0.8804 3.4496
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.943597 0.088508 112.346 < 2e-16 ***
## category_code_LT01_1_count 0.452808 0.086679 5.224 2.59e-07 ***
## category_code_LT01_2_count 0.847979 0.072461 11.703 < 2e-16 ***
## category_code_LT01_5_count 1.005001 0.061516 16.337 < 2e-16 ***
## category_code_LT01_12_count -0.005328 0.209539 -0.025 0.980
## category_code_LT01_13_count 0.026635 0.248315 0.107 0.915
## category_code_LT01_15_count -0.272606 0.770688 -0.354 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.616337672738308
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0257 -0.7972 0.0062 0.8918 3.4486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.944592 0.088491 112.380 < 2e-16 ***
## category_code_LT01_1_count 0.447216 0.084835 5.272 2.03e-07 ***
## category_code_LT01_2_count 0.842533 0.072895 11.558 < 2e-16 ***
## category_code_LT01_5_count 1.004468 0.061529 16.325 < 2e-16 ***
## category_code_LT01_12_count -0.001269 0.209376 -0.006 0.995
## category_code_LT01_13_count 0.037342 0.247488 0.151 0.880
## category_code_LT01_16_count 0.366133 1.180355 0.310 0.757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6163
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616469263842759
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0250 -0.7936 0.0211 0.8848 3.4478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94533 0.08862 112.229 < 2e-16 ***
## category_code_LT01_1_count 0.45008 0.08607 5.230 2.52e-07 ***
## category_code_LT01_2_count 0.84539 0.07269 11.631 < 2e-16 ***
## category_code_LT01_5_count 1.00238 0.06190 16.193 < 2e-16 ***
## category_code_LT01_12_count -0.01024 0.20990 -0.049 0.961
## category_code_LT01_14_count 0.12859 0.33084 0.389 0.698
## category_code_LT01_15_count -0.27871 0.76748 -0.363 0.717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616449404700036
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0257 -0.7941 0.0069 0.8966 3.4468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.946397 0.088602 112.259 < 2e-16 ***
## category_code_LT01_1_count 0.444782 0.084461 5.266 2.09e-07 ***
## category_code_LT01_2_count 0.839802 0.073194 11.474 < 2e-16 ***
## category_code_LT01_5_count 1.001800 0.061923 16.178 < 2e-16 ***
## category_code_LT01_12_count -0.006229 0.209730 -0.030 0.976
## category_code_LT01_14_count 0.134911 0.331321 0.407 0.684
## category_code_LT01_16_count 0.385339 1.181079 0.326 0.744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616418476192768
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0252 -0.7982 0.0169 0.8845 3.4494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.943726 0.088498 112.361 < 2e-16 ***
## category_code_LT01_1_count 0.455142 0.085375 5.331 1.49e-07 ***
## category_code_LT01_2_count 0.845617 0.072933 11.594 < 2e-16 ***
## category_code_LT01_5_count 1.004935 0.061467 16.349 < 2e-16 ***
## category_code_LT01_12_count -0.004258 0.209546 -0.020 0.984
## category_code_LT01_15_count -0.272802 0.767916 -0.355 0.723
## category_code_LT01_16_count 0.346138 1.180038 0.293 0.769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616477287853673
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0247 -0.7932 0.0197 0.8874 3.4477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94548 0.08862 112.221 < 2e-16 ***
## category_code_LT01_1_count 0.44778 0.08663 5.169 3.43e-07 ***
## category_code_LT01_2_count 0.84400 0.07152 11.802 < 2e-16 ***
## category_code_LT01_5_count 1.00177 0.06164 16.253 < 2e-16 ***
## category_code_LT01_13_count 0.02793 0.24830 0.112 0.910
## category_code_LT01_14_count 0.12811 0.33024 0.388 0.698
## category_code_LT01_15_count -0.26930 0.76990 -0.350 0.727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616467972552182
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.7938 0.0071 0.8992 3.4466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94657 0.08861 112.255 < 2e-16 ***
## category_code_LT01_1_count 0.44230 0.08486 5.212 2.75e-07 ***
## category_code_LT01_2_count 0.83844 0.07205 11.637 < 2e-16 ***
## category_code_LT01_5_count 1.00117 0.06166 16.237 < 2e-16 ***
## category_code_LT01_13_count 0.03886 0.24747 0.157 0.875
## category_code_LT01_14_count 0.13509 0.33073 0.408 0.683
## category_code_LT01_16_count 0.39272 1.18182 0.332 0.740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616429211584869
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0250 -0.7966 0.0146 0.8872 3.4493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94388 0.08850 112.356 < 2e-16 ***
## category_code_LT01_1_count 0.45309 0.08580 5.281 1.94e-07 ***
## category_code_LT01_2_count 0.84455 0.07165 11.788 < 2e-16 ***
## category_code_LT01_5_count 1.00448 0.06114 16.428 < 2e-16 ***
## category_code_LT01_13_count 0.02956 0.24849 0.119 0.905
## category_code_LT01_15_count -0.26375 0.77039 -0.342 0.732
## category_code_LT01_16_count 0.35212 1.18082 0.298 0.766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616545042560932
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0248 -0.7932 0.0175 0.8918 3.4475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94569 0.08861 112.235 < 2e-16 ***
## category_code_LT01_1_count 0.45013 0.08527 5.279 1.96e-07 ***
## category_code_LT01_2_count 0.84138 0.07202 11.683 < 2e-16 ***
## category_code_LT01_5_count 1.00160 0.06159 16.263 < 2e-16 ***
## category_code_LT01_14_count 0.13329 0.33067 0.403 0.687
## category_code_LT01_15_count -0.26940 0.76709 -0.351 0.726
## category_code_LT01_16_count 0.37251 1.18146 0.315 0.753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.635903488524998
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9669 -0.7422 0.0710 0.8519 3.4843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95260 0.08614 115.533 < 2e-16 ***
## category_code_LT01_1_count 0.22942 0.08814 2.603 0.00953 **
## category_code_LT01_3_count 0.29197 0.11189 2.609 0.00934 **
## category_code_LT01_4_count 0.65694 0.09542 6.885 1.78e-11 ***
## category_code_LT01_5_count 0.90811 0.06140 14.789 < 2e-16 ***
## category_code_LT01_6_count 0.41904 0.14835 2.825 0.00493 **
## category_code_LT01_7_count 0.45732 0.15140 3.021 0.00265 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6403, Adjusted R-squared: 0.6359
## F-statistic: 145.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.629549056205586
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9792 -0.7418 0.0440 0.8987 3.4647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95224 0.08692 114.502 < 2e-16 ***
## category_code_LT01_1_count 0.25492 0.08859 2.878 0.00418 **
## category_code_LT01_3_count 0.30730 0.11277 2.725 0.00666 **
## category_code_LT01_4_count 0.73724 0.09236 7.982 1.03e-14 ***
## category_code_LT01_5_count 0.92631 0.06249 14.824 < 2e-16 ***
## category_code_LT01_6_count 0.42582 0.14973 2.844 0.00464 **
## category_code_LT01_8_count -0.20059 0.27164 -0.738 0.46061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.634, Adjusted R-squared: 0.6295
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.631221710101217
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9648 -0.7587 0.0470 0.9201 3.4845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94735 0.08672 114.710 < 2e-16 ***
## category_code_LT01_1_count 0.24819 0.08841 2.807 0.00519 **
## category_code_LT01_3_count 0.27834 0.11374 2.447 0.01475 *
## category_code_LT01_4_count 0.72679 0.09239 7.866 2.35e-14 ***
## category_code_LT01_5_count 0.91267 0.06182 14.764 < 2e-16 ***
## category_code_LT01_6_count 0.40884 0.14951 2.735 0.00647 **
## category_code_LT01_9_count 0.37334 0.22413 1.666 0.09640 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6357, Adjusted R-squared: 0.6312
## F-statistic: 142.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.629461169840905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9578 -0.7577 0.0588 0.8991 3.4065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93510 0.09008 110.287 < 2e-16 ***
## category_code_LT01_1_count 0.25593 0.08867 2.887 0.00407 **
## category_code_LT01_3_count 0.29309 0.11453 2.559 0.01079 *
## category_code_LT01_4_count 0.73671 0.09239 7.974 1.09e-14 ***
## category_code_LT01_5_count 0.92022 0.06183 14.882 < 2e-16 ***
## category_code_LT01_6_count 0.40666 0.15147 2.685 0.00751 **
## category_code_LT01_10_count 0.07464 0.11400 0.655 0.51294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6339, Adjusted R-squared: 0.6295
## F-statistic: 141.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.634463460827613
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9794 -0.7501 0.0706 0.9020 3.4691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96054 0.08639 115.295 < 2e-16 ***
## category_code_LT01_1_count 0.21722 0.08899 2.441 0.01500 *
## category_code_LT01_3_count 0.23545 0.11509 2.046 0.04131 *
## category_code_LT01_4_count 0.63731 0.09918 6.426 3.1e-10 ***
## category_code_LT01_5_count 0.91468 0.06143 14.889 < 2e-16 ***
## category_code_LT01_6_count 0.35876 0.15051 2.384 0.01752 *
## category_code_LT01_11_count 0.30656 0.11462 2.675 0.00773 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.362 on 491 degrees of freedom
## Multiple R-squared: 0.6389, Adjusted R-squared: 0.6345
## F-statistic: 144.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.629138789645216
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9728 -0.7402 0.0603 0.9014 3.4739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.950659 0.086940 114.455 < 2e-16 ***
## category_code_LT01_1_count 0.252791 0.089165 2.835 0.00477 **
## category_code_LT01_3_count 0.305924 0.113004 2.707 0.00702 **
## category_code_LT01_4_count 0.737842 0.092628 7.966 1.15e-14 ***
## category_code_LT01_5_count 0.919353 0.062103 14.804 < 2e-16 ***
## category_code_LT01_6_count 0.421311 0.150691 2.796 0.00538 **
## category_code_LT01_12_count 0.007957 0.205858 0.039 0.96918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6336, Adjusted R-squared: 0.6291
## F-statistic: 141.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.629155812014575
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9728 -0.7403 0.0632 0.9015 3.4739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95075 0.08694 114.456 < 2e-16 ***
## category_code_LT01_1_count 0.25124 0.08948 2.808 0.00519 **
## category_code_LT01_3_count 0.30611 0.11282 2.713 0.00690 **
## category_code_LT01_4_count 0.73703 0.09266 7.955 1.25e-14 ***
## category_code_LT01_5_count 0.91920 0.06190 14.851 < 2e-16 ***
## category_code_LT01_6_count 0.42248 0.14976 2.821 0.00498 **
## category_code_LT01_13_count 0.03771 0.24328 0.155 0.87687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6336, Adjusted R-squared: 0.6292
## F-statistic: 141.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.629340848787112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9721 -0.7422 0.0660 0.9009 3.4761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95265 0.08700 114.397 < 2e-16 ***
## category_code_LT01_1_count 0.24786 0.08917 2.780 0.00565 **
## category_code_LT01_3_count 0.30878 0.11291 2.735 0.00647 **
## category_code_LT01_4_count 0.72934 0.09391 7.767 4.74e-14 ***
## category_code_LT01_5_count 0.91554 0.06232 14.690 < 2e-16 ***
## category_code_LT01_6_count 0.42912 0.15032 2.855 0.00449 **
## category_code_LT01_14_count 0.16998 0.32764 0.519 0.60413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6338, Adjusted R-squared: 0.6293
## F-statistic: 141.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.629407703806436
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9712 -0.7484 0.0615 0.9030 3.4756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94932 0.08694 114.444 < 2e-16 ***
## category_code_LT01_1_count 0.26175 0.08972 2.917 0.00369 **
## category_code_LT01_3_count 0.31338 0.11343 2.763 0.00595 **
## category_code_LT01_4_count 0.73757 0.09238 7.984 1.01e-14 ***
## category_code_LT01_5_count 0.91898 0.06184 14.861 < 2e-16 ***
## category_code_LT01_6_count 0.42448 0.14973 2.835 0.00477 **
## category_code_LT01_15_count -0.45328 0.75781 -0.598 0.55002
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6339, Adjusted R-squared: 0.6294
## F-statistic: 141.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.629860476790333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9718 -0.7402 0.0426 0.9034 3.4754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95064 0.08685 114.567 < 2e-16 ***
## category_code_LT01_1_count 0.25539 0.08855 2.884 0.00410 **
## category_code_LT01_3_count 0.29100 0.11377 2.558 0.01084 *
## category_code_LT01_4_count 0.73638 0.09233 7.975 1.08e-14 ***
## category_code_LT01_5_count 0.91803 0.06181 14.852 < 2e-16 ***
## category_code_LT01_6_count 0.43297 0.15000 2.886 0.00407 **
## category_code_LT01_16_count 1.13888 1.16307 0.979 0.32796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6343, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.630384887468357
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0012 -0.7584 0.0442 0.8470 3.4394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96880 0.08667 115.016 < 2e-16 ***
## category_code_LT01_1_count 0.24964 0.08860 2.818 0.00503 **
## category_code_LT01_3_count 0.32721 0.11210 2.919 0.00367 **
## category_code_LT01_4_count 0.72916 0.09256 7.878 2.16e-14 ***
## category_code_LT01_5_count 0.93423 0.06217 15.027 < 2e-16 ***
## category_code_LT01_7_count 0.46328 0.15260 3.036 0.00253 **
## category_code_LT01_8_count -0.19715 0.27128 -0.727 0.46773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6348, Adjusted R-squared: 0.6304
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.631719271763418
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9870 -0.7312 0.0163 0.8775 3.4589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96356 0.08652 115.165 < 2e-16 ***
## category_code_LT01_1_count 0.24404 0.08844 2.759 0.00601 **
## category_code_LT01_3_count 0.30019 0.11315 2.653 0.00824 **
## category_code_LT01_4_count 0.72159 0.09254 7.797 3.82e-14 ***
## category_code_LT01_5_count 0.92123 0.06151 14.978 < 2e-16 ***
## category_code_LT01_7_count 0.43691 0.15303 2.855 0.00449 **
## category_code_LT01_9_count 0.34158 0.22479 1.520 0.12926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6362, Adjusted R-squared: 0.6317
## F-statistic: 143.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.630516559844338
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9746 -0.7362 0.0307 0.8680 3.3792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94667 0.08998 110.540 < 2e-16 ***
## category_code_LT01_1_count 0.25111 0.08863 2.833 0.00480 **
## category_code_LT01_3_count 0.30807 0.11406 2.701 0.00715 **
## category_code_LT01_4_count 0.72662 0.09262 7.846 2.71e-14 ***
## category_code_LT01_5_count 0.92771 0.06147 15.093 < 2e-16 ***
## category_code_LT01_7_count 0.44957 0.15303 2.938 0.00346 **
## category_code_LT01_10_count 0.09464 0.11285 0.839 0.40208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.635, Adjusted R-squared: 0.6305
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.63435952358078
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9982 -0.7553 0.0581 0.8637 3.4471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97383 0.08622 115.680 < 2e-16 ***
## category_code_LT01_1_count 0.21696 0.08901 2.438 0.0151 *
## category_code_LT01_3_count 0.25883 0.11486 2.253 0.0247 *
## category_code_LT01_4_count 0.64313 0.09877 6.511 1.84e-10 ***
## category_code_LT01_5_count 0.92263 0.06118 15.081 < 2e-16 ***
## category_code_LT01_7_count 0.36813 0.15640 2.354 0.0190 *
## category_code_LT01_11_count 0.28277 0.11670 2.423 0.0158 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.362 on 491 degrees of freedom
## Multiple R-squared: 0.6388, Adjusted R-squared: 0.6344
## F-statistic: 144.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.63007942342419
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9933 -0.7497 0.0389 0.8655 3.4510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96700 0.08668 114.990 < 2e-16 ***
## category_code_LT01_1_count 0.24420 0.08924 2.737 0.00643 **
## category_code_LT01_3_count 0.32321 0.11239 2.876 0.00421 **
## category_code_LT01_4_count 0.72665 0.09305 7.809 3.51e-14 ***
## category_code_LT01_5_count 0.92530 0.06183 14.966 < 2e-16 ***
## category_code_LT01_7_count 0.45997 0.15260 3.014 0.00271 **
## category_code_LT01_12_count 0.07143 0.20428 0.350 0.72673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.6301
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.630028245431468
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9950 -0.7492 0.0245 0.8585 3.4482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96691 0.08669 114.977 < 2e-16 ***
## category_code_LT01_1_count 0.25062 0.08936 2.805 0.00524 **
## category_code_LT01_3_count 0.32579 0.11214 2.905 0.00383 **
## category_code_LT01_4_count 0.73067 0.09265 7.886 2.04e-14 ***
## category_code_LT01_5_count 0.92793 0.06153 15.080 < 2e-16 ***
## category_code_LT01_7_count 0.46397 0.15350 3.022 0.00264 **
## category_code_LT01_13_count -0.05696 0.24435 -0.233 0.81578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.629990628288333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7499 0.0272 0.8603 3.4487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96737 0.08679 114.840 < 2e-16 ***
## category_code_LT01_1_count 0.24732 0.08908 2.776 0.00571 **
## category_code_LT01_3_count 0.32632 0.11233 2.905 0.00384 **
## category_code_LT01_4_count 0.72905 0.09346 7.800 3.73e-14 ***
## category_code_LT01_5_count 0.92705 0.06189 14.978 < 2e-16 ***
## category_code_LT01_7_count 0.45947 0.15293 3.004 0.00280 **
## category_code_LT01_14_count 0.02170 0.32664 0.066 0.94706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.630093245855235
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9939 -0.7439 0.0457 0.8606 3.4495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96630 0.08670 114.950 < 2e-16 ***
## category_code_LT01_1_count 0.25351 0.08984 2.822 0.00497 **
## category_code_LT01_3_count 0.33062 0.11284 2.930 0.00355 **
## category_code_LT01_4_count 0.73033 0.09259 7.887 2.02e-14 ***
## category_code_LT01_5_count 0.92728 0.06151 15.076 < 2e-16 ***
## category_code_LT01_7_count 0.45737 0.15278 2.994 0.00290 **
## category_code_LT01_15_count -0.28413 0.75768 -0.375 0.70782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6346, Adjusted R-squared: 0.6301
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.63043093647932
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9946 -0.7506 0.0304 0.8614 3.4491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96738 0.08664 115.048 < 2e-16 ***
## category_code_LT01_1_count 0.25003 0.08860 2.822 0.00497 **
## category_code_LT01_3_count 0.31474 0.11301 2.785 0.00556 **
## category_code_LT01_4_count 0.73005 0.09255 7.889 2e-14 ***
## category_code_LT01_5_count 0.92670 0.06148 15.072 < 2e-16 ***
## category_code_LT01_7_count 0.46019 0.15253 3.017 0.00269 **
## category_code_LT01_16_count 0.88973 1.15892 0.768 0.44302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6349, Adjusted R-squared: 0.6304
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.625968577685659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9974 -0.7549 0.0347 0.8840 3.4419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96273 0.08721 114.233 < 2e-16 ***
## category_code_LT01_1_count 0.26764 0.08883 3.013 0.00272 **
## category_code_LT01_3_count 0.31002 0.11400 2.719 0.00677 **
## category_code_LT01_4_count 0.79695 0.08936 8.919 < 2e-16 ***
## category_code_LT01_5_count 0.93743 0.06258 14.980 < 2e-16 ***
## category_code_LT01_8_count -0.18847 0.27291 -0.691 0.49015
## category_code_LT01_9_count 0.41028 0.22551 1.819 0.06946 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.624352831336426
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9803 -0.7749 0.0435 0.8656 3.3202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94020 0.09071 109.581 < 2e-16 ***
## category_code_LT01_1_count 0.27711 0.08902 3.113 0.00196 **
## category_code_LT01_3_count 0.31803 0.11498 2.766 0.00589 **
## category_code_LT01_4_count 0.80518 0.08938 9.008 < 2e-16 ***
## category_code_LT01_5_count 0.94529 0.06257 15.108 < 2e-16 ***
## category_code_LT01_8_count -0.17980 0.27343 -0.658 0.51113
## category_code_LT01_10_count 0.12342 0.11343 1.088 0.27707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.630456299325513
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0085 -0.7648 0.0495 0.8665 3.4317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97523 0.08671 115.045 < 2e-16 ***
## category_code_LT01_1_count 0.22942 0.08941 2.566 0.0106 *
## category_code_LT01_3_count 0.25550 0.11549 2.212 0.0274 *
## category_code_LT01_4_count 0.68513 0.09771 7.012 7.84e-12 ***
## category_code_LT01_5_count 0.93540 0.06214 15.053 < 2e-16 ***
## category_code_LT01_8_count -0.14754 0.27128 -0.544 0.5868
## category_code_LT01_11_count 0.34750 0.11387 3.052 0.0024 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6349, Adjusted R-squared: 0.6305
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.623554880665425
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0051 -0.7624 0.0190 0.8616 3.4320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96666 0.08747 113.947 < 2e-16 ***
## category_code_LT01_1_count 0.26961 0.08969 3.006 0.00278 **
## category_code_LT01_3_count 0.33878 0.11329 2.990 0.00293 **
## category_code_LT01_4_count 0.80837 0.08976 9.006 < 2e-16 ***
## category_code_LT01_5_count 0.94289 0.06293 14.983 < 2e-16 ***
## category_code_LT01_8_count -0.17697 0.27381 -0.646 0.51836
## category_code_LT01_12_count 0.07735 0.20618 0.375 0.70770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6236
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.623449039940587
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0067 -0.7663 0.0244 0.8376 3.4296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96675 0.08748 113.929 < 2e-16 ***
## category_code_LT01_1_count 0.27296 0.08996 3.034 0.00254 **
## category_code_LT01_3_count 0.34165 0.11304 3.022 0.00264 **
## category_code_LT01_4_count 0.81158 0.08950 9.068 < 2e-16 ***
## category_code_LT01_5_count 0.94502 0.06271 15.070 < 2e-16 ***
## category_code_LT01_8_count -0.17279 0.27425 -0.630 0.52894
## category_code_LT01_13_count 0.01277 0.24557 0.052 0.95856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.623500378183179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0066 -0.7632 0.0315 0.8556 3.4303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96788 0.08758 113.809 < 2e-16 ***
## category_code_LT01_1_count 0.27107 0.08958 3.026 0.00261 **
## category_code_LT01_3_count 0.34330 0.11320 3.033 0.00255 **
## category_code_LT01_4_count 0.80808 0.09044 8.935 < 2e-16 ***
## category_code_LT01_5_count 0.94331 0.06303 14.965 < 2e-16 ***
## category_code_LT01_8_count -0.17459 0.27371 -0.638 0.52386
## category_code_LT01_14_count 0.08679 0.32884 0.264 0.79195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6235
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.62364814859698
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0054 -0.7702 0.0277 0.8405 3.4310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96564 0.08748 113.916 < 2e-16 ***
## category_code_LT01_1_count 0.28111 0.09023 3.115 0.00195 **
## category_code_LT01_3_count 0.34805 0.11370 3.061 0.00233 **
## category_code_LT01_4_count 0.81183 0.08925 9.096 < 2e-16 ***
## category_code_LT01_5_count 0.94474 0.06263 15.084 < 2e-16 ***
## category_code_LT01_8_count -0.17314 0.27363 -0.633 0.52721
## category_code_LT01_15_count -0.39109 0.76338 -0.512 0.60866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6236
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.62392628719247
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0068 -0.7625 0.0167 0.8388 3.4296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96711 0.08743 114.006 < 2e-16 ***
## category_code_LT01_1_count 0.27592 0.08906 3.098 0.00206 **
## category_code_LT01_3_count 0.33013 0.11391 2.898 0.00392 **
## category_code_LT01_4_count 0.81208 0.08922 9.102 < 2e-16 ***
## category_code_LT01_5_count 0.94469 0.06261 15.089 < 2e-16 ***
## category_code_LT01_8_count -0.18425 0.27386 -0.673 0.50138
## category_code_LT01_16_count 0.92592 1.17046 0.791 0.42928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.6239
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.626212282951528
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9700 -0.7442 0.0197 0.9112 3.3602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93949 0.09047 109.868 < 2e-16 ***
## category_code_LT01_1_count 0.26905 0.08883 3.029 0.00259 **
## category_code_LT01_3_count 0.29118 0.11569 2.517 0.01216 *
## category_code_LT01_4_count 0.79253 0.08949 8.856 < 2e-16 ***
## category_code_LT01_5_count 0.93138 0.06187 15.054 < 2e-16 ***
## category_code_LT01_9_count 0.38434 0.22659 1.696 0.09048 .
## category_code_LT01_10_count 0.10158 0.11375 0.893 0.37232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6262
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.632242773700026
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7521 0.0562 0.8728 3.4503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97006 0.08650 115.258 < 2e-16 ***
## category_code_LT01_1_count 0.22367 0.08918 2.508 0.01246 *
## category_code_LT01_3_count 0.22827 0.11624 1.964 0.05012 .
## category_code_LT01_4_count 0.67515 0.09764 6.915 1.47e-11 ***
## category_code_LT01_5_count 0.92317 0.06142 15.030 < 2e-16 ***
## category_code_LT01_9_count 0.36669 0.22389 1.638 0.10210
## category_code_LT01_11_count 0.33857 0.11373 2.977 0.00306 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 491 degrees of freedom
## Multiple R-squared: 0.6367, Adjusted R-squared: 0.6322
## F-statistic: 143.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.625695596240473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7506 0.0353 0.9059 3.4530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96107 0.08722 114.211 < 2e-16 ***
## category_code_LT01_1_count 0.26222 0.08947 2.931 0.00354 **
## category_code_LT01_3_count 0.30640 0.11429 2.681 0.00759 **
## category_code_LT01_4_count 0.79406 0.08988 8.835 < 2e-16 ***
## category_code_LT01_5_count 0.92884 0.06223 14.925 < 2e-16 ***
## category_code_LT01_9_count 0.40515 0.22549 1.797 0.07299 .
## category_code_LT01_12_count 0.07073 0.20549 0.344 0.73083
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.625639778014328
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9911 -0.7508 0.0425 0.8938 3.4510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96129 0.08723 114.202 < 2e-16 ***
## category_code_LT01_1_count 0.26321 0.08973 2.933 0.00351 **
## category_code_LT01_3_count 0.30875 0.11405 2.707 0.00702 **
## category_code_LT01_4_count 0.79582 0.08966 8.876 < 2e-16 ***
## category_code_LT01_5_count 0.93049 0.06197 15.016 < 2e-16 ***
## category_code_LT01_9_count 0.40886 0.22601 1.809 0.07106 .
## category_code_LT01_13_count 0.05210 0.24492 0.213 0.83162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.62561743470361
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9913 -0.7513 0.0440 0.8951 3.4509
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96172 0.08734 114.053 < 2e-16 ***
## category_code_LT01_1_count 0.26472 0.08933 2.963 0.00319 **
## category_code_LT01_3_count 0.30996 0.11429 2.712 0.00692 **
## category_code_LT01_4_count 0.79552 0.09047 8.793 < 2e-16 ***
## category_code_LT01_5_count 0.93016 0.06229 14.932 < 2e-16 ***
## category_code_LT01_9_count 0.40357 0.22611 1.785 0.07490 .
## category_code_LT01_14_count 0.04152 0.32876 0.126 0.89955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.625751689650218
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9903 -0.7498 0.0565 0.8912 3.4517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96028 0.08723 114.181 < 2e-16 ***
## category_code_LT01_1_count 0.27237 0.09004 3.025 0.00262 **
## category_code_LT01_3_count 0.31481 0.11479 2.743 0.00632 **
## category_code_LT01_4_count 0.79737 0.08938 8.921 < 2e-16 ***
## category_code_LT01_5_count 0.93076 0.06191 15.034 < 2e-16 ***
## category_code_LT01_9_count 0.40134 0.22569 1.778 0.07597 .
## category_code_LT01_15_count -0.33396 0.76196 -0.438 0.66137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.625988958811919
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9912 -0.7510 0.0312 0.9060 3.4510
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96147 0.08718 114.259 < 2e-16 ***
## category_code_LT01_1_count 0.26794 0.08884 3.016 0.00269 **
## category_code_LT01_3_count 0.29900 0.11486 2.603 0.00952 **
## category_code_LT01_4_count 0.79761 0.08935 8.926 < 2e-16 ***
## category_code_LT01_5_count 0.93036 0.06189 15.032 < 2e-16 ***
## category_code_LT01_9_count 0.40104 0.22549 1.778 0.07594 .
## category_code_LT01_16_count 0.82778 1.16634 0.710 0.47821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.63098053204451
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9795 -0.7462 0.0451 0.8825 3.3396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94980 0.08996 110.604 < 2e-16 ***
## category_code_LT01_1_count 0.23129 0.08937 2.588 0.00994 **
## category_code_LT01_3_count 0.23325 0.11723 1.990 0.04718 *
## category_code_LT01_4_count 0.67962 0.09776 6.952 1.15e-11 ***
## category_code_LT01_5_count 0.93026 0.06138 15.157 < 2e-16 ***
## category_code_LT01_10_count 0.11209 0.11244 0.997 0.31932
## category_code_LT01_11_count 0.34621 0.11378 3.043 0.00247 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.631
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.624096047467934
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9736 -0.7664 0.0101 0.8876 3.3335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93922 0.09073 109.552 < 2e-16 ***
## category_code_LT01_1_count 0.27196 0.08967 3.033 0.00255 **
## category_code_LT01_3_count 0.31490 0.11521 2.733 0.00650 **
## category_code_LT01_4_count 0.80257 0.08988 8.929 < 2e-16 ***
## category_code_LT01_5_count 0.93710 0.06220 15.065 < 2e-16 ***
## category_code_LT01_10_count 0.12044 0.11354 1.061 0.28932
## category_code_LT01_12_count 0.06409 0.20610 0.311 0.75597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.624024405039944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9748 -0.7672 0.0064 0.8715 3.3302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93907 0.09075 109.526 < 2e-16 ***
## category_code_LT01_1_count 0.27465 0.08990 3.055 0.00237 **
## category_code_LT01_3_count 0.31706 0.11502 2.757 0.00606 **
## category_code_LT01_4_count 0.80508 0.08963 8.982 < 2e-16 ***
## category_code_LT01_5_count 0.93896 0.06192 15.164 < 2e-16 ***
## category_code_LT01_10_count 0.12168 0.11352 1.072 0.28429
## category_code_LT01_13_count 0.01367 0.24503 0.056 0.95555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.624022068393146
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9748 -0.7686 0.0063 0.8804 3.3302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.939059 0.091313 108.846 < 2e-16 ***
## category_code_LT01_1_count 0.275267 0.089656 3.070 0.00226 **
## category_code_LT01_3_count 0.317115 0.115593 2.743 0.00630 **
## category_code_LT01_4_count 0.805317 0.090425 8.906 < 2e-16 ***
## category_code_LT01_5_count 0.939029 0.062315 15.069 < 2e-16 ***
## category_code_LT01_10_count 0.121683 0.116619 1.043 0.29727
## category_code_LT01_14_count 0.002597 0.337764 0.008 0.99387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.624287730365187
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9722 -0.7638 0.0329 0.8742 3.3277
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93677 0.09078 109.463 < 2e-16 ***
## category_code_LT01_1_count 0.28411 0.09022 3.149 0.00174 **
## category_code_LT01_3_count 0.32352 0.11550 2.801 0.00530 **
## category_code_LT01_4_count 0.80509 0.08939 9.006 < 2e-16 ***
## category_code_LT01_5_count 0.93861 0.06187 15.172 < 2e-16 ***
## category_code_LT01_10_count 0.12642 0.11367 1.112 0.26662
## category_code_LT01_15_count -0.45048 0.76447 -0.589 0.55595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6288, Adjusted R-squared: 0.6243
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.624430792376867
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9750 -0.7637 0.0077 0.8831 3.3325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93974 0.09069 109.603 < 2e-16 ***
## category_code_LT01_1_count 0.27732 0.08901 3.116 0.00194 **
## category_code_LT01_3_count 0.30674 0.11581 2.649 0.00834 **
## category_code_LT01_4_count 0.80571 0.08938 9.015 < 2e-16 ***
## category_code_LT01_5_count 0.93831 0.06186 15.169 < 2e-16 ***
## category_code_LT01_10_count 0.11966 0.11343 1.055 0.29198
## category_code_LT01_16_count 0.85436 1.16871 0.731 0.46511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.630361268473329
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0057 -0.7640 0.0384 0.8507 3.4355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97441 0.08669 115.052 < 2e-16 ***
## category_code_LT01_1_count 0.23078 0.08967 2.574 0.01036 *
## category_code_LT01_3_count 0.25426 0.11547 2.202 0.02813 *
## category_code_LT01_4_count 0.68415 0.09773 7.001 8.41e-12 ***
## category_code_LT01_5_count 0.93264 0.06170 15.116 < 2e-16 ***
## category_code_LT01_11_count 0.36138 0.11745 3.077 0.00221 **
## category_code_LT01_12_count -0.08674 0.21070 -0.412 0.68075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6348, Adjusted R-squared: 0.6304
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.63023383375046
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0037 -0.7636 0.0431 0.8732 3.4385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.974003 0.086707 115.031 < 2e-16 ***
## category_code_LT01_1_count 0.227931 0.090173 2.528 0.01179 *
## category_code_LT01_3_count 0.253939 0.115494 2.199 0.02836 *
## category_code_LT01_4_count 0.684607 0.097855 6.996 8.66e-12 ***
## category_code_LT01_5_count 0.930289 0.061474 15.133 < 2e-16 ***
## category_code_LT01_11_count 0.349512 0.113916 3.068 0.00227 **
## category_code_LT01_13_count -0.003477 0.243012 -0.014 0.98859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6347, Adjusted R-squared: 0.6302
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.630261034312342
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0036 -0.7644 0.0445 0.8742 3.4392
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97482 0.08681 114.910 < 2e-16 ***
## category_code_LT01_1_count 0.22599 0.08987 2.515 0.0122 *
## category_code_LT01_3_count 0.25523 0.11568 2.206 0.0278 *
## category_code_LT01_4_count 0.68197 0.09866 6.913 1.49e-11 ***
## category_code_LT01_5_count 0.92892 0.06184 15.022 < 2e-16 ***
## category_code_LT01_11_count 0.34898 0.11387 3.065 0.0023 **
## category_code_LT01_14_count 0.06212 0.32593 0.191 0.8489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6347, Adjusted R-squared: 0.6303
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.630454646031431
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0023 -0.7625 0.0452 0.8654 3.4401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97290 0.08670 115.024 < 2e-16 ***
## category_code_LT01_1_count 0.23556 0.09051 2.602 0.00954 **
## category_code_LT01_3_count 0.26054 0.11610 2.244 0.02526 *
## category_code_LT01_4_count 0.68430 0.09771 7.003 8.26e-12 ***
## category_code_LT01_5_count 0.92982 0.06142 15.138 < 2e-16 ***
## category_code_LT01_11_count 0.34990 0.11382 3.074 0.00223 **
## category_code_LT01_15_count -0.40988 0.75646 -0.542 0.58818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6349, Adjusted R-squared: 0.6305
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.630628239705316
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0034 -0.7638 0.0443 0.8711 3.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97426 0.08666 115.099 < 2e-16 ***
## category_code_LT01_1_count 0.22990 0.08939 2.572 0.01041 *
## category_code_LT01_3_count 0.24371 0.11629 2.096 0.03662 *
## category_code_LT01_4_count 0.68511 0.09769 7.013 7.75e-12 ***
## category_code_LT01_5_count 0.92953 0.06141 15.136 < 2e-16 ***
## category_code_LT01_11_count 0.34833 0.11380 3.061 0.00233 **
## category_code_LT01_16_count 0.83915 1.15871 0.724 0.46928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6351, Adjusted R-squared: 0.6306
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.623240489006466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7548 0.0176 0.8502 3.4402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96521 0.08748 113.919 < 2e-16 ***
## category_code_LT01_1_count 0.26706 0.09055 2.949 0.00333 **
## category_code_LT01_3_count 0.33766 0.11332 2.980 0.00303 **
## category_code_LT01_4_count 0.80817 0.09001 8.979 < 2e-16 ***
## category_code_LT01_5_count 0.93673 0.06231 15.033 < 2e-16 ***
## category_code_LT01_12_count 0.07288 0.20617 0.354 0.72385
## category_code_LT01_13_count 0.02148 0.24517 0.088 0.93021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.623276065600209
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7557 -0.0053 0.8652 3.4408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96616 0.08758 113.797 < 2e-16 ***
## category_code_LT01_1_count 0.26606 0.09014 2.952 0.00331 **
## category_code_LT01_3_count 0.33922 0.11352 2.988 0.00295 **
## category_code_LT01_4_count 0.80548 0.09086 8.865 < 2e-16 ***
## category_code_LT01_5_count 0.93535 0.06264 14.933 < 2e-16 ***
## category_code_LT01_12_count 0.06985 0.20662 0.338 0.73547
## category_code_LT01_14_count 0.07664 0.32967 0.232 0.81626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6233
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.623425823474816
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9982 -0.7554 0.0331 0.8613 3.4414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96410 0.08748 113.907 < 2e-16 ***
## category_code_LT01_1_count 0.27570 0.09094 3.031 0.00256 **
## category_code_LT01_3_count 0.34408 0.11402 3.018 0.00268 **
## category_code_LT01_4_count 0.80885 0.08978 9.010 < 2e-16 ***
## category_code_LT01_5_count 0.93666 0.06226 15.044 < 2e-16 ***
## category_code_LT01_12_count 0.06850 0.20631 0.332 0.74003
## category_code_LT01_15_count -0.38167 0.76436 -0.499 0.61777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.623678315381507
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9991 -0.7550 0.0010 0.8764 3.4407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96543 0.08742 113.991 < 2e-16 ***
## category_code_LT01_1_count 0.27022 0.08969 3.013 0.00272 **
## category_code_LT01_3_count 0.32648 0.11421 2.859 0.00444 **
## category_code_LT01_4_count 0.80885 0.08975 9.013 < 2e-16 ***
## category_code_LT01_5_count 0.93608 0.06225 15.038 < 2e-16 ***
## category_code_LT01_12_count 0.07396 0.20604 0.359 0.71979
## category_code_LT01_16_count 0.88983 1.16947 0.761 0.44710
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.623195414765725
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7560 0.0253 0.8479 3.4386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96642 0.08759 113.784 < 2e-16 ***
## category_code_LT01_1_count 0.26827 0.09047 2.965 0.00317 **
## category_code_LT01_3_count 0.34199 0.11323 3.020 0.00266 **
## category_code_LT01_4_count 0.80772 0.09071 8.904 < 2e-16 ***
## category_code_LT01_5_count 0.93712 0.06241 15.016 < 2e-16 ***
## category_code_LT01_13_count 0.02347 0.24520 0.096 0.92379
## category_code_LT01_14_count 0.08466 0.32899 0.257 0.79702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.623342892041582
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9997 -0.7567 0.0259 0.8480 3.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96419 0.08749 113.888 < 2e-16 ***
## category_code_LT01_1_count 0.27886 0.09129 3.055 0.00238 **
## category_code_LT01_3_count 0.34678 0.11374 3.049 0.00242 **
## category_code_LT01_4_count 0.81169 0.08951 9.068 < 2e-16 ***
## category_code_LT01_5_count 0.93866 0.06198 15.144 < 2e-16 ***
## category_code_LT01_13_count 0.01127 0.24611 0.046 0.96350
## category_code_LT01_15_count -0.38985 0.76677 -0.508 0.61138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6233
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.623589899214743
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0007 -0.7552 0.0251 0.8464 3.4383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96561 0.08744 113.975 < 2e-16 ***
## category_code_LT01_1_count 0.27263 0.08991 3.032 0.00256 **
## category_code_LT01_3_count 0.32922 0.11395 2.889 0.00403 **
## category_code_LT01_4_count 0.81150 0.08948 9.069 < 2e-16 ***
## category_code_LT01_5_count 0.93811 0.06197 15.139 < 2e-16 ***
## category_code_LT01_13_count 0.02846 0.24516 0.116 0.90763
## category_code_LT01_16_count 0.89186 1.17019 0.762 0.44634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6236
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.623391409226731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9996 -0.7548 0.0321 0.8547 3.4400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96526 0.08759 113.774 < 2e-16 ***
## category_code_LT01_1_count 0.27701 0.09075 3.053 0.00239 **
## category_code_LT01_3_count 0.34841 0.11389 3.059 0.00234 **
## category_code_LT01_4_count 0.80827 0.09045 8.936 < 2e-16 ***
## category_code_LT01_5_count 0.93693 0.06235 15.027 < 2e-16 ***
## category_code_LT01_14_count 0.08407 0.32887 0.256 0.79834
## category_code_LT01_15_count -0.39288 0.76363 -0.514 0.60715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.623642632691261
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0006 -0.7563 0.0147 0.8712 3.4391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96677 0.08754 113.859 < 2e-16 ***
## category_code_LT01_1_count 0.27132 0.08956 3.029 0.00258 **
## category_code_LT01_3_count 0.33089 0.11408 2.900 0.00389 **
## category_code_LT01_4_count 0.80807 0.09043 8.936 < 2e-16 ***
## category_code_LT01_5_count 0.93630 0.06234 15.019 < 2e-16 ***
## category_code_LT01_14_count 0.09438 0.32902 0.287 0.77436
## category_code_LT01_16_count 0.90104 1.17046 0.770 0.44178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6236
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.623759963238788
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9995 -0.7540 0.0212 0.8551 3.4396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96449 0.08744 113.960 < 2e-16 ***
## category_code_LT01_1_count 0.28112 0.09021 3.116 0.00194 **
## category_code_LT01_3_count 0.33563 0.11467 2.927 0.00358 **
## category_code_LT01_4_count 0.81215 0.08924 9.101 < 2e-16 ***
## category_code_LT01_5_count 0.93800 0.06192 15.149 < 2e-16 ***
## category_code_LT01_15_count -0.37063 0.76386 -0.485 0.62775
## category_code_LT01_16_count 0.86502 1.17025 0.739 0.46015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6238
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.601447994408199
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0445 -0.8250 0.0262 0.9134 3.6604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97879 0.09008 110.776 < 2e-16 ***
## category_code_LT01_1_count 0.44103 0.08653 5.097 4.94e-07 ***
## category_code_LT01_3_count 0.60512 0.10699 5.656 2.63e-08 ***
## category_code_LT01_5_count 0.98218 0.06412 15.317 < 2e-16 ***
## category_code_LT01_6_count 0.69970 0.14949 4.681 3.71e-06 ***
## category_code_LT01_7_count 0.75413 0.15204 4.960 9.72e-07 ***
## category_code_LT01_8_count -0.26056 0.28181 -0.925 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 491 degrees of freedom
## Multiple R-squared: 0.6063, Adjusted R-squared: 0.6014
## F-statistic: 126 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.60283595125245
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0281 -0.8360 0.0455 0.9489 3.6800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97311 0.08993 110.903 < 2e-16 ***
## category_code_LT01_1_count 0.43369 0.08644 5.017 7.34e-07 ***
## category_code_LT01_3_count 0.57426 0.10847 5.294 1.81e-07 ***
## category_code_LT01_5_count 0.96675 0.06350 15.224 < 2e-16 ***
## category_code_LT01_6_count 0.67961 0.14949 4.546 6.89e-06 ***
## category_code_LT01_7_count 0.72237 0.15276 4.729 2.96e-06 ***
## category_code_LT01_9_count 0.37467 0.23354 1.604 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6076, Adjusted R-squared: 0.6028
## F-statistic: 126.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.600875715822414
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0274 -0.8153 0.0372 0.9394 3.6814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96719 0.09346 106.646 < 2e-16 ***
## category_code_LT01_1_count 0.44154 0.08673 5.091 5.08e-07 ***
## category_code_LT01_3_count 0.59679 0.10898 5.476 6.95e-08 ***
## category_code_LT01_5_count 0.97425 0.06352 15.336 < 2e-16 ***
## category_code_LT01_6_count 0.68613 0.15148 4.530 7.43e-06 ***
## category_code_LT01_7_count 0.74577 0.15266 4.885 1.40e-06 ***
## category_code_LT01_10_count 0.04591 0.11871 0.387 0.699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6057, Adjusted R-squared: 0.6009
## F-statistic: 125.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.613140111671994
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0351 -0.7941 0.0641 0.9160 3.4068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98633 0.08876 112.515 < 2e-16 ***
## category_code_LT01_1_count 0.35397 0.08793 4.026 6.58e-05 ***
## category_code_LT01_3_count 0.44477 0.11286 3.941 9.29e-05 ***
## category_code_LT01_5_count 0.95776 0.06266 15.286 < 2e-16 ***
## category_code_LT01_6_count 0.55000 0.15172 3.625 0.000319 ***
## category_code_LT01_7_count 0.54731 0.15830 3.457 0.000593 ***
## category_code_LT01_11_count 0.45714 0.11530 3.965 8.44e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6178, Adjusted R-squared: 0.6131
## F-statistic: 132.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.600929923338074
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.8267 0.0305 0.9300 3.6718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97683 0.09011 110.715 < 2e-16 ***
## category_code_LT01_1_count 0.43378 0.08746 4.960 9.73e-07 ***
## category_code_LT01_3_count 0.60014 0.10750 5.583 3.92e-08 ***
## category_code_LT01_5_count 0.97073 0.06383 15.208 < 2e-16 ***
## category_code_LT01_6_count 0.68600 0.15091 4.546 6.90e-06 ***
## category_code_LT01_7_count 0.74929 0.15213 4.925 1.15e-06 ***
## category_code_LT01_12_count 0.09910 0.21308 0.465 0.642
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6057, Adjusted R-squared: 0.6009
## F-statistic: 125.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.600763232237565
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0365 -0.8248 0.0253 0.9247 3.6651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97682 0.09013 110.691 < 2e-16 ***
## category_code_LT01_1_count 0.43814 0.08763 5.000 8.00e-07 ***
## category_code_LT01_3_count 0.60446 0.10709 5.644 2.81e-08 ***
## category_code_LT01_5_count 0.97345 0.06357 15.314 < 2e-16 ***
## category_code_LT01_6_count 0.69570 0.14956 4.652 4.24e-06 ***
## category_code_LT01_7_count 0.74879 0.15330 4.884 1.41e-06 ***
## category_code_LT01_13_count 0.02685 0.25369 0.106 0.916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6056, Adjusted R-squared: 0.6008
## F-statistic: 125.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.602197982769861
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0325 -0.8167 0.0384 0.9369 3.6537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98125 0.09003 110.864 < 2e-16 ***
## category_code_LT01_1_count 0.41961 0.08772 4.784 2.28e-06 ***
## category_code_LT01_3_count 0.60210 0.10690 5.632 3.00e-08 ***
## category_code_LT01_5_count 0.96134 0.06408 15.003 < 2e-16 ***
## category_code_LT01_6_count 0.70575 0.14948 4.721 3.06e-06 ***
## category_code_LT01_7_count 0.72806 0.15280 4.765 2.49e-06 ***
## category_code_LT01_14_count 0.44855 0.33600 1.335 0.183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.421 on 491 degrees of freedom
## Multiple R-squared: 0.607, Adjusted R-squared: 0.6022
## F-statistic: 126.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.600889704272981
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0354 -0.8248 0.0170 0.9237 3.6649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97582 0.09015 110.662 < 2e-16 ***
## category_code_LT01_1_count 0.44586 0.08794 5.070 5.64e-07 ***
## category_code_LT01_3_count 0.60995 0.10784 5.656 2.64e-08 ***
## category_code_LT01_5_count 0.97339 0.06351 15.326 < 2e-16 ***
## category_code_LT01_6_count 0.69738 0.14960 4.662 4.05e-06 ***
## category_code_LT01_7_count 0.74776 0.15228 4.910 1.24e-06 ***
## category_code_LT01_15_count -0.32155 0.78733 -0.408 0.683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6057, Adjusted R-squared: 0.6009
## F-statistic: 125.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.60167193558739
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0352 -0.8242 0.0290 0.9289 3.6573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97669 0.09003 110.817 < 2e-16 ***
## category_code_LT01_1_count 0.44144 0.08651 5.103 4.79e-07 ***
## category_code_LT01_3_count 0.58665 0.10829 5.417 9.49e-08 ***
## category_code_LT01_5_count 0.97178 0.06347 15.310 < 2e-16 ***
## category_code_LT01_6_count 0.70708 0.14978 4.721 3.07e-06 ***
## category_code_LT01_7_count 0.74997 0.15195 4.935 1.10e-06 ***
## category_code_LT01_16_count 1.28313 1.20635 1.064 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.421 on 491 degrees of freedom
## Multiple R-squared: 0.6065, Adjusted R-squared: 0.6017
## F-statistic: 126.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.604013115044621
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0569 -0.7826 0.0503 0.9203 3.4017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99150 0.08981 111.246 < 2e-16 ***
## category_code_LT01_1_count 0.38943 0.08847 4.402 1.32e-05 ***
## category_code_LT01_3_count 0.46264 0.11411 4.054 5.85e-05 ***
## category_code_LT01_5_count 0.98173 0.06388 15.368 < 2e-16 ***
## category_code_LT01_6_count 0.55585 0.15363 3.618 0.000328 ***
## category_code_LT01_8_count -0.16892 0.28105 -0.601 0.548093
## category_code_LT01_11_count 0.58377 0.11043 5.286 1.88e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6088, Adjusted R-squared: 0.604
## F-statistic: 127.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.606271317011426
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0415 -0.7972 0.0686 0.9377 3.4073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98583 0.08957 111.490 < 2e-16 ***
## category_code_LT01_1_count 0.38096 0.08824 4.317 1.91e-05 ***
## category_code_LT01_3_count 0.42954 0.11512 3.731 0.000213 ***
## category_code_LT01_5_count 0.96768 0.06316 15.322 < 2e-16 ***
## category_code_LT01_6_count 0.53699 0.15329 3.503 0.000502 ***
## category_code_LT01_9_count 0.41298 0.23161 1.783 0.075189 .
## category_code_LT01_11_count 0.57166 0.11034 5.181 3.23e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.611, Adjusted R-squared: 0.6063
## F-statistic: 128.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.604164857437361
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0338 -0.7821 0.0326 0.9333 3.4212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97197 0.09309 107.119 < 2e-16 ***
## category_code_LT01_1_count 0.39047 0.08848 4.413 1.25e-05 ***
## category_code_LT01_3_count 0.44527 0.11602 3.838 0.000140 ***
## category_code_LT01_5_count 0.97663 0.06316 15.463 < 2e-16 ***
## category_code_LT01_6_count 0.53371 0.15544 3.434 0.000646 ***
## category_code_LT01_10_count 0.08733 0.11780 0.741 0.458833
## category_code_LT01_11_count 0.58534 0.11034 5.305 1.71e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6089, Adjusted R-squared: 0.6042
## F-statistic: 127.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.604153947408186
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0546 -0.7801 0.0279 0.9286 3.4026
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99055 0.08977 111.285 < 2e-16 ***
## category_code_LT01_1_count 0.39240 0.08864 4.427 1.18e-05 ***
## category_code_LT01_3_count 0.46045 0.11406 4.037 6.28e-05 ***
## category_code_LT01_5_count 0.97982 0.06337 15.462 < 2e-16 ***
## category_code_LT01_6_count 0.56037 0.15389 3.641 0.0003 ***
## category_code_LT01_11_count 0.60649 0.11369 5.334 1.47e-07 ***
## category_code_LT01_12_count -0.16008 0.21864 -0.732 0.4644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6089, Adjusted R-squared: 0.6042
## F-statistic: 127.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.603838869539471
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0510 -0.7946 0.0371 0.9270 3.4028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99035 0.08981 111.239 < 2e-16 ***
## category_code_LT01_1_count 0.38232 0.08951 4.271 2.34e-05 ***
## category_code_LT01_3_count 0.46052 0.11411 4.036 6.31e-05 ***
## category_code_LT01_5_count 0.97489 0.06325 15.414 < 2e-16 ***
## category_code_LT01_6_count 0.55303 0.15355 3.602 0.000349 ***
## category_code_LT01_11_count 0.58370 0.11060 5.278 1.97e-07 ***
## category_code_LT01_13_count 0.09572 0.25127 0.381 0.703402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6086, Adjusted R-squared: 0.6038
## F-statistic: 127.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.605185496418345
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0470 -0.7972 0.0262 0.9178 3.3988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99438 0.08971 111.409 < 2e-16 ***
## category_code_LT01_1_count 0.36848 0.08941 4.121 4.42e-05 ***
## category_code_LT01_3_count 0.46175 0.11391 4.054 5.87e-05 ***
## category_code_LT01_5_count 0.96334 0.06377 15.107 < 2e-16 ***
## category_code_LT01_6_count 0.56575 0.15361 3.683 0.000256 ***
## category_code_LT01_11_count 0.57115 0.11076 5.156 3.66e-07 ***
## category_code_LT01_14_count 0.45116 0.33439 1.349 0.177897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 491 degrees of freedom
## Multiple R-squared: 0.61, Adjusted R-squared: 0.6052
## F-statistic: 128 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.604059379423528
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0496 -0.7861 0.0282 0.9269 3.4044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98873 0.08981 111.215 < 2e-16 ***
## category_code_LT01_1_count 0.39698 0.08959 4.431 1.16e-05 ***
## category_code_LT01_3_count 0.46878 0.11471 4.087 5.11e-05 ***
## category_code_LT01_5_count 0.97527 0.06317 15.438 < 2e-16 ***
## category_code_LT01_6_count 0.55456 0.15354 3.612 0.000335 ***
## category_code_LT01_11_count 0.58632 0.11034 5.314 1.63e-07 ***
## category_code_LT01_15_count -0.50680 0.78326 -0.647 0.517910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6088, Adjusted R-squared: 0.6041
## F-statistic: 127.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.60441639099864
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0504 -0.7797 0.0533 0.9264 3.4031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99010 0.08974 111.318 < 2e-16 ***
## category_code_LT01_1_count 0.38991 0.08840 4.411 1.27e-05 ***
## category_code_LT01_3_count 0.44653 0.11508 3.880 0.000119 ***
## category_code_LT01_5_count 0.97446 0.06315 15.430 < 2e-16 ***
## category_code_LT01_6_count 0.56315 0.15389 3.659 0.000280 ***
## category_code_LT01_11_count 0.58321 0.11035 5.285 1.89e-07 ***
## category_code_LT01_16_count 1.11679 1.20275 0.929 0.353591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6092, Adjusted R-squared: 0.6044
## F-statistic: 127.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.603060461281095
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0876 -0.8023 0.1131 0.8617 3.3804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01271 0.08966 111.670 < 2e-16 ***
## category_code_LT01_1_count 0.39264 0.08852 4.435 1.13e-05 ***
## category_code_LT01_3_count 0.50280 0.11328 4.439 1.12e-05 ***
## category_code_LT01_5_count 0.99547 0.06358 15.657 < 2e-16 ***
## category_code_LT01_7_count 0.55311 0.16047 3.447 0.000616 ***
## category_code_LT01_8_count -0.16402 0.28135 -0.583 0.560185
## category_code_LT01_11_count 0.55553 0.11342 4.898 1.32e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.6031
## F-statistic: 126.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.604972663100464
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0724 -0.8085 0.1105 0.8792 3.3864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00680 0.08946 111.859 < 2e-16 ***
## category_code_LT01_1_count 0.38531 0.08832 4.363 1.57e-05 ***
## category_code_LT01_3_count 0.47105 0.11441 4.117 4.50e-05 ***
## category_code_LT01_5_count 0.98213 0.06285 15.626 < 2e-16 ***
## category_code_LT01_7_count 0.52371 0.16072 3.258 0.0012 **
## category_code_LT01_9_count 0.38373 0.23275 1.649 0.0998 .
## category_code_LT01_11_count 0.54809 0.11322 4.841 1.73e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6097, Adjusted R-squared: 0.605
## F-statistic: 127.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.603614433714735
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0564 -0.7907 0.0647 0.8854 3.4076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98556 0.09311 107.240 < 2e-16 ***
## category_code_LT01_1_count 0.39392 0.08846 4.453 1.05e-05 ***
## category_code_LT01_3_count 0.47750 0.11550 4.134 4.19e-05 ***
## category_code_LT01_5_count 0.98968 0.06279 15.762 < 2e-16 ***
## category_code_LT01_7_count 0.53560 0.16082 3.330 0.000932 ***
## category_code_LT01_10_count 0.11835 0.11682 1.013 0.311475
## category_code_LT01_11_count 0.55553 0.11327 4.904 1.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6084, Adjusted R-squared: 0.6036
## F-statistic: 127.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.602837005987263
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0837 -0.8012 0.1120 0.8654 3.3816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01160 0.08966 111.657 < 2e-16 ***
## category_code_LT01_1_count 0.39283 0.08885 4.421 1.21e-05 ***
## category_code_LT01_3_count 0.50112 0.11327 4.424 1.19e-05 ***
## category_code_LT01_5_count 0.99138 0.06316 15.697 < 2e-16 ***
## category_code_LT01_7_count 0.54700 0.16070 3.404 0.000719 ***
## category_code_LT01_11_count 0.56628 0.11777 4.809 2.03e-06 ***
## category_code_LT01_12_count -0.05510 0.21881 -0.252 0.801297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6076, Adjusted R-squared: 0.6028
## F-statistic: 126.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.602788380283109
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0824 -0.8009 0.1118 0.8662 3.3818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01131 0.08967 111.652 < 2e-16 ***
## category_code_LT01_1_count 0.39156 0.08940 4.380 1.45e-05 ***
## category_code_LT01_3_count 0.50097 0.11327 4.423 1.20e-05 ***
## category_code_LT01_5_count 0.98993 0.06289 15.741 < 2e-16 ***
## category_code_LT01_7_count 0.55050 0.16137 3.411 0.000699 ***
## category_code_LT01_11_count 0.55838 0.11339 4.925 1.16e-06 ***
## category_code_LT01_13_count -0.01452 0.25308 -0.057 0.954275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6076, Adjusted R-squared: 0.6028
## F-statistic: 126.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.603333394011179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0805 -0.8039 0.1122 0.8693 3.3788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01434 0.08968 111.673 < 2e-16 ***
## category_code_LT01_1_count 0.38037 0.08935 4.257 2.48e-05 ***
## category_code_LT01_3_count 0.50258 0.11321 4.439 1.12e-05 ***
## category_code_LT01_5_count 0.98292 0.06337 15.512 < 2e-16 ***
## category_code_LT01_7_count 0.53851 0.16085 3.348 0.000876 ***
## category_code_LT01_11_count 0.55306 0.11346 4.875 1.47e-06 ***
## category_code_LT01_14_count 0.27631 0.33559 0.823 0.410703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6081, Adjusted R-squared: 0.6033
## F-statistic: 127 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.602901285287662
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0813 -0.8000 0.1081 0.8703 3.3826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01055 0.08968 111.631 < 2e-16 ***
## category_code_LT01_1_count 0.39662 0.08980 4.417 1.23e-05 ***
## category_code_LT01_3_count 0.50574 0.11397 4.438 1.12e-05 ***
## category_code_LT01_5_count 0.98957 0.06285 15.746 < 2e-16 ***
## category_code_LT01_7_count 0.54638 0.16059 3.402 0.000723 ***
## category_code_LT01_11_count 0.55921 0.11337 4.932 1.11e-06 ***
## category_code_LT01_15_count -0.29681 0.78518 -0.378 0.705583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6077, Adjusted R-squared: 0.6029
## F-statistic: 126.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.603140529596933
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0821 -0.8012 0.1128 0.8642 3.3815
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01161 0.08962 111.706 < 2e-16 ***
## category_code_LT01_1_count 0.39298 0.08852 4.439 1.11e-05 ***
## category_code_LT01_3_count 0.49142 0.11413 4.306 2.01e-05 ***
## category_code_LT01_5_count 0.98916 0.06283 15.743 < 2e-16 ***
## category_code_LT01_7_count 0.55007 0.16033 3.431 0.000653 ***
## category_code_LT01_11_count 0.55730 0.11332 4.918 1.19e-06 ***
## category_code_LT01_16_count 0.79575 1.20103 0.663 0.507927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6079, Adjusted R-squared: 0.6031
## F-statistic: 126.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.631326525977464
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9928 -0.7582 0.0474 0.8725 3.8974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96429 0.08663 115.023 < 2e-16 ***
## category_code_LT01_1_count 0.25997 0.08803 2.953 0.00330 **
## category_code_LT01_4_count 0.75687 0.08776 8.624 < 2e-16 ***
## category_code_LT01_5_count 0.92852 0.06224 14.918 < 2e-16 ***
## category_code_LT01_6_count 0.46485 0.14852 3.130 0.00185 **
## category_code_LT01_7_count 0.47740 0.15228 3.135 0.00182 **
## category_code_LT01_8_count -0.21503 0.27108 -0.793 0.42802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6313
## F-statistic: 142.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.633105160979736
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9762 -0.7692 0.0304 0.8887 3.9094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95806 0.08643 115.216 < 2e-16 ***
## category_code_LT01_1_count 0.25162 0.08787 2.864 0.00437 **
## category_code_LT01_4_count 0.74129 0.08807 8.417 4.25e-16 ***
## category_code_LT01_5_count 0.91359 0.06159 14.834 < 2e-16 ***
## category_code_LT01_6_count 0.44321 0.14840 2.987 0.00296 **
## category_code_LT01_7_count 0.44619 0.15268 2.922 0.00363 **
## category_code_LT01_9_count 0.38593 0.22235 1.736 0.08325 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6375, Adjusted R-squared: 0.6331
## F-statistic: 143.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.631396297382489
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9658 -0.7511 0.0345 0.8972 3.8143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94204 0.08989 110.607 < 2e-16 ***
## category_code_LT01_1_count 0.26063 0.08804 2.960 0.00322 **
## category_code_LT01_4_count 0.75243 0.08799 8.551 < 2e-16 ***
## category_code_LT01_5_count 0.92169 0.06157 14.969 < 2e-16 ***
## category_code_LT01_6_count 0.43872 0.15062 2.913 0.00375 **
## category_code_LT01_7_count 0.46247 0.15280 3.027 0.00260 **
## category_code_LT01_10_count 0.09552 0.11239 0.850 0.39581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6314
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.635640799092721
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9893 -0.7494 0.0534 0.9021 3.7159
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96936 0.08613 115.743 < 2e-16 ***
## category_code_LT01_1_count 0.22249 0.08860 2.511 0.01236 *
## category_code_LT01_4_count 0.65651 0.09595 6.842 2.33e-11 ***
## category_code_LT01_5_count 0.91613 0.06125 14.957 < 2e-16 ***
## category_code_LT01_6_count 0.39183 0.15001 2.612 0.00928 **
## category_code_LT01_7_count 0.37567 0.15619 2.405 0.01653 *
## category_code_LT01_11_count 0.29195 0.11495 2.540 0.01140 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.36 on 491 degrees of freedom
## Multiple R-squared: 0.64, Adjusted R-squared: 0.6356
## F-statistic: 145.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.630877939017708
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9853 -0.7521 0.0584 0.8889 3.9017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96255 0.08665 114.971 < 2e-16 ***
## category_code_LT01_1_count 0.25629 0.08868 2.890 0.00402 **
## category_code_LT01_4_count 0.75649 0.08822 8.575 < 2e-16 ***
## category_code_LT01_5_count 0.92029 0.06189 14.870 < 2e-16 ***
## category_code_LT01_6_count 0.45740 0.14958 3.058 0.00235 **
## category_code_LT01_7_count 0.47380 0.15231 3.111 0.00197 **
## category_code_LT01_12_count 0.03655 0.20505 0.178 0.85861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.630876303875989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7520 0.0453 0.8849 3.8991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96242 0.08666 114.967 < 2e-16 ***
## category_code_LT01_1_count 0.26018 0.08883 2.929 0.00356 **
## category_code_LT01_4_count 0.75867 0.08789 8.632 < 2e-16 ***
## category_code_LT01_5_count 0.92167 0.06165 14.951 < 2e-16 ***
## category_code_LT01_6_count 0.45999 0.14855 3.096 0.00207 **
## category_code_LT01_7_count 0.47675 0.15320 3.112 0.00197 **
## category_code_LT01_13_count -0.04200 0.24413 -0.172 0.86349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.630885382131593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7529 0.0536 0.8887 3.9009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96335 0.08674 114.859 < 2e-16 ***
## category_code_LT01_1_count 0.25629 0.08853 2.895 0.00396 **
## category_code_LT01_4_count 0.75530 0.08880 8.506 < 2e-16 ***
## category_code_LT01_5_count 0.91985 0.06204 14.826 < 2e-16 ***
## category_code_LT01_6_count 0.46356 0.14923 3.106 0.00200 **
## category_code_LT01_7_count 0.47197 0.15260 3.093 0.00210 **
## category_code_LT01_14_count 0.06681 0.32727 0.204 0.83834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.630876319927833
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7518 0.0452 0.8851 3.8988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96222 0.08667 114.942 < 2e-16 ***
## category_code_LT01_1_count 0.26089 0.08946 2.916 0.00371 **
## category_code_LT01_4_count 0.75881 0.08793 8.630 < 2e-16 ***
## category_code_LT01_5_count 0.92129 0.06161 14.952 < 2e-16 ***
## category_code_LT01_6_count 0.46161 0.14863 3.106 0.00201 **
## category_code_LT01_7_count 0.47278 0.15245 3.101 0.00204 **
## category_code_LT01_15_count -0.12954 0.75274 -0.172 0.86344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.632172813232769
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9833 -0.7514 0.0522 0.8916 3.9004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96183 0.08650 115.163 < 2e-16 ***
## category_code_LT01_1_count 0.25914 0.08790 2.948 0.00335 **
## category_code_LT01_4_count 0.74869 0.08793 8.514 < 2e-16 ***
## category_code_LT01_5_count 0.91835 0.06155 14.921 < 2e-16 ***
## category_code_LT01_6_count 0.47241 0.14852 3.181 0.00156 **
## category_code_LT01_7_count 0.47279 0.15204 3.110 0.00198 **
## category_code_LT01_16_count 1.52402 1.14866 1.327 0.18520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 491 degrees of freedom
## Multiple R-squared: 0.6366, Adjusted R-squared: 0.6322
## F-statistic: 143.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.627162515465525
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7750 0.0375 0.9196 3.8927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95748 0.08715 114.258 < 2e-16 ***
## category_code_LT01_1_count 0.27660 0.08825 3.134 0.00183 **
## category_code_LT01_4_count 0.82080 0.08436 9.730 < 2e-16 ***
## category_code_LT01_5_count 0.93083 0.06265 14.859 < 2e-16 ***
## category_code_LT01_6_count 0.44884 0.14969 2.999 0.00285 **
## category_code_LT01_8_count -0.20718 0.27261 -0.760 0.44762
## category_code_LT01_9_count 0.45894 0.22301 2.058 0.04013 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6317, Adjusted R-squared: 0.6272
## F-statistic: 140.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.62491140842673
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9723 -0.7688 0.0596 0.9541 3.7670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93542 0.09065 109.598 < 2e-16 ***
## category_code_LT01_1_count 0.28853 0.08842 3.263 0.00118 **
## category_code_LT01_4_count 0.83664 0.08415 9.943 < 2e-16 ***
## category_code_LT01_5_count 0.94060 0.06266 15.010 < 2e-16 ***
## category_code_LT01_6_count 0.44042 0.15202 2.897 0.00393 **
## category_code_LT01_8_count -0.19591 0.27335 -0.717 0.47391
## category_code_LT01_10_count 0.12693 0.11295 1.124 0.26168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6249
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.63161436210721
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0004 -0.7606 0.0626 0.8781 3.6594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97109 0.08663 115.094 < 2e-16 ***
## category_code_LT01_1_count 0.23537 0.08902 2.644 0.00846 **
## category_code_LT01_4_count 0.69975 0.09482 7.379 6.84e-13 ***
## category_code_LT01_5_count 0.92979 0.06219 14.951 < 2e-16 ***
## category_code_LT01_6_count 0.38329 0.15088 2.540 0.01138 *
## category_code_LT01_8_count -0.16159 0.27101 -0.596 0.55129
## category_code_LT01_11_count 0.35819 0.11205 3.197 0.00148 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6361, Adjusted R-squared: 0.6316
## F-statistic: 143 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.62398056927731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9985 -0.7723 0.0467 0.9438 3.8826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96260 0.08749 113.877 < 2e-16 ***
## category_code_LT01_1_count 0.28382 0.08914 3.184 0.00154 **
## category_code_LT01_4_count 0.84509 0.08421 10.036 < 2e-16 ***
## category_code_LT01_5_count 0.93916 0.06299 14.909 < 2e-16 ***
## category_code_LT01_6_count 0.46577 0.15105 3.084 0.00216 **
## category_code_LT01_8_count -0.19220 0.27377 -0.702 0.48298
## category_code_LT01_12_count 0.04351 0.20704 0.210 0.83362
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.623957747137569
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9991 -0.7664 0.0361 0.9477 3.8820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96262 0.08749 113.872 < 2e-16 ***
## category_code_LT01_1_count 0.28452 0.08943 3.181 0.00156 **
## category_code_LT01_4_count 0.84609 0.08404 10.067 < 2e-16 ***
## category_code_LT01_5_count 0.93999 0.06281 14.965 < 2e-16 ***
## category_code_LT01_6_count 0.46989 0.15002 3.132 0.00184 **
## category_code_LT01_8_count -0.18850 0.27419 -0.687 0.49211
## category_code_LT01_13_count 0.02942 0.24545 0.120 0.90464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.624072511306694
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9988 -0.7774 0.0430 0.9389 3.8821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96422 0.08757 113.785 < 2e-16 ***
## category_code_LT01_1_count 0.28210 0.08903 3.169 0.00163 **
## category_code_LT01_4_count 0.84078 0.08510 9.879 < 2e-16 ***
## category_code_LT01_5_count 0.93733 0.06317 14.837 < 2e-16 ***
## category_code_LT01_6_count 0.47549 0.15068 3.156 0.00170 **
## category_code_LT01_8_count -0.19221 0.27365 -0.702 0.48276
## category_code_LT01_14_count 0.13362 0.32967 0.405 0.68543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.624016195708244
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9986 -0.7672 0.0389 0.9444 3.8783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96201 0.08750 113.851 < 2e-16 ***
## category_code_LT01_1_count 0.29074 0.08986 3.236 0.00130 **
## category_code_LT01_4_count 0.84796 0.08382 10.117 < 2e-16 ***
## category_code_LT01_5_count 0.94020 0.06274 14.986 < 2e-16 ***
## category_code_LT01_6_count 0.47131 0.15009 3.140 0.00179 **
## category_code_LT01_8_count -0.19017 0.27364 -0.695 0.48740
## category_code_LT01_15_count -0.22858 0.75899 -0.301 0.76342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.625380322540276
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9970 -0.7755 0.0440 0.9573 3.8808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96199 0.08732 114.082 < 2e-16 ***
## category_code_LT01_1_count 0.28718 0.08834 3.251 0.00123 **
## category_code_LT01_4_count 0.83694 0.08391 9.974 < 2e-16 ***
## category_code_LT01_5_count 0.93787 0.06265 14.970 < 2e-16 ***
## category_code_LT01_6_count 0.48227 0.14999 3.215 0.00139 **
## category_code_LT01_8_count -0.21038 0.27353 -0.769 0.44219
## category_code_LT01_16_count 1.59119 1.16082 1.371 0.17108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.627303627404525
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9606 -0.7821 0.0370 0.9370 3.8060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93490 0.09035 109.964 < 2e-16 ***
## category_code_LT01_1_count 0.27718 0.08824 3.141 0.00178 **
## category_code_LT01_4_count 0.81487 0.08468 9.623 < 2e-16 ***
## category_code_LT01_5_count 0.92451 0.06198 14.917 < 2e-16 ***
## category_code_LT01_6_count 0.42321 0.15162 2.791 0.00546 **
## category_code_LT01_9_count 0.43006 0.22454 1.915 0.05603 .
## category_code_LT01_10_count 0.09912 0.11342 0.874 0.38259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.633747231617325
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9847 -0.7609 0.0697 0.9014 3.6809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96490 0.08640 115.334 < 2e-16 ***
## category_code_LT01_1_count 0.22758 0.08878 2.563 0.01066 *
## category_code_LT01_4_count 0.68341 0.09495 7.197 2.31e-12 ***
## category_code_LT01_5_count 0.91598 0.06149 14.897 < 2e-16 ***
## category_code_LT01_6_count 0.36528 0.15053 2.427 0.01559 *
## category_code_LT01_9_count 0.39763 0.22170 1.794 0.07350 .
## category_code_LT01_11_count 0.34380 0.11204 3.068 0.00227 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.363 on 491 degrees of freedom
## Multiple R-squared: 0.6382, Adjusted R-squared: 0.6337
## F-statistic: 144.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.626746660185078
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9805 -0.8010 0.0561 0.9300 3.8968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95588 0.08717 114.210 < 2e-16 ***
## category_code_LT01_1_count 0.27295 0.08890 3.070 0.00226 **
## category_code_LT01_4_count 0.82005 0.08483 9.667 < 2e-16 ***
## category_code_LT01_5_count 0.92292 0.06229 14.816 < 2e-16 ***
## category_code_LT01_6_count 0.44183 0.15075 2.931 0.00354 **
## category_code_LT01_9_count 0.45357 0.22305 2.034 0.04254 *
## category_code_LT01_12_count 0.03566 0.20620 0.173 0.86275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6267
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.626790300174743
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9807 -0.7941 0.0564 0.9280 3.8981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95600 0.08717 114.216 < 2e-16 ***
## category_code_LT01_1_count 0.27097 0.08920 3.038 0.00251 **
## category_code_LT01_4_count 0.81923 0.08476 9.666 < 2e-16 ***
## category_code_LT01_5_count 0.92313 0.06207 14.871 < 2e-16 ***
## category_code_LT01_6_count 0.44571 0.14969 2.977 0.00305 **
## category_code_LT01_9_count 0.45817 0.22349 2.050 0.04089 *
## category_code_LT01_13_count 0.07227 0.24456 0.296 0.76772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.626772690245584
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9810 -0.7953 0.0589 0.9278 3.8961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95693 0.08727 114.092 < 2e-16 ***
## category_code_LT01_1_count 0.27239 0.08877 3.069 0.00227 **
## category_code_LT01_4_count 0.81793 0.08559 9.556 < 2e-16 ***
## category_code_LT01_5_count 0.92210 0.06244 14.769 < 2e-16 ***
## category_code_LT01_6_count 0.44881 0.15046 2.983 0.00300 **
## category_code_LT01_9_count 0.44991 0.22359 2.012 0.04474 *
## category_code_LT01_14_count 0.08340 0.32927 0.253 0.80014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.626770240523491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9807 -0.7791 0.0521 0.9212 3.8933
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95542 0.08719 114.186 < 2e-16 ***
## category_code_LT01_1_count 0.27865 0.08964 3.109 0.00199 **
## category_code_LT01_4_count 0.82247 0.08448 9.736 < 2e-16 ***
## category_code_LT01_5_count 0.92386 0.06202 14.897 < 2e-16 ***
## category_code_LT01_6_count 0.44646 0.14980 2.980 0.00302 **
## category_code_LT01_9_count 0.45232 0.22312 2.027 0.04318 *
## category_code_LT01_15_count -0.18675 0.75651 -0.247 0.80512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.627873528222063
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9789 -0.7941 0.0649 0.9320 3.8953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95536 0.08704 114.376 < 2e-16 ***
## category_code_LT01_1_count 0.27593 0.08814 3.131 0.00185 **
## category_code_LT01_4_count 0.81345 0.08453 9.624 < 2e-16 ***
## category_code_LT01_5_count 0.92143 0.06196 14.872 < 2e-16 ***
## category_code_LT01_6_count 0.45663 0.14976 3.049 0.00242 **
## category_code_LT01_9_count 0.43956 0.22301 1.971 0.04928 *
## category_code_LT01_16_count 1.42485 1.15691 1.232 0.21869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6324, Adjusted R-squared: 0.6279
## F-statistic: 140.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.63206752109405
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9719 -0.7519 0.0607 0.9076 3.5648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94620 0.08985 110.702 < 2e-16 ***
## category_code_LT01_1_count 0.23635 0.08896 2.657 0.00815 **
## category_code_LT01_4_count 0.69230 0.09502 7.286 1.28e-12 ***
## category_code_LT01_5_count 0.92446 0.06146 15.043 < 2e-16 ***
## category_code_LT01_6_count 0.35543 0.15265 2.328 0.02030 *
## category_code_LT01_10_count 0.10974 0.11196 0.980 0.32750
## category_code_LT01_11_count 0.35555 0.11203 3.174 0.00160 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 491 degrees of freedom
## Multiple R-squared: 0.6365, Adjusted R-squared: 0.6321
## F-statistic: 143.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.62453689942745
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9659 -0.7815 0.0626 0.9625 3.7728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93427 0.09069 109.547 < 2e-16 ***
## category_code_LT01_1_count 0.28504 0.08908 3.200 0.00146 **
## category_code_LT01_4_count 0.83590 0.08461 9.880 < 2e-16 ***
## category_code_LT01_5_count 0.93307 0.06229 14.980 < 2e-16 ***
## category_code_LT01_6_count 0.43420 0.15298 2.838 0.00472 **
## category_code_LT01_10_count 0.12493 0.11306 1.105 0.26968
## category_code_LT01_12_count 0.03163 0.20692 0.153 0.87856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.62453030479267
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9664 -0.7729 0.0602 0.9608 3.7726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93432 0.09069 109.536 < 2e-16 ***
## category_code_LT01_1_count 0.28514 0.08934 3.192 0.00151 **
## category_code_LT01_4_count 0.83638 0.08445 9.904 < 2e-16 ***
## category_code_LT01_5_count 0.93368 0.06205 15.046 < 2e-16 ***
## category_code_LT01_6_count 0.43735 0.15208 2.876 0.00420 **
## category_code_LT01_10_count 0.12500 0.11307 1.105 0.26950
## category_code_LT01_13_count 0.02975 0.24496 0.121 0.90340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.624532890637701
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9670 -0.7731 0.0639 0.9507 3.7749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93549 0.09124 108.892 < 2e-16 ***
## category_code_LT01_1_count 0.28526 0.08906 3.203 0.00145 **
## category_code_LT01_4_count 0.83539 0.08524 9.800 < 2e-16 ***
## category_code_LT01_5_count 0.93292 0.06250 14.927 < 2e-16 ***
## category_code_LT01_6_count 0.43971 0.15351 2.864 0.00436 **
## category_code_LT01_10_count 0.12179 0.11633 1.047 0.29566
## category_code_LT01_14_count 0.04566 0.33916 0.135 0.89295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.6246389472956
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9648 -0.7655 0.0504 0.9496 3.7643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93262 0.09075 109.454 < 2e-16 ***
## category_code_LT01_1_count 0.29294 0.08982 3.262 0.00119 **
## category_code_LT01_4_count 0.83825 0.08422 9.953 < 2e-16 ***
## category_code_LT01_5_count 0.93380 0.06200 15.061 < 2e-16 ***
## category_code_LT01_6_count 0.43835 0.15204 2.883 0.00411 **
## category_code_LT01_10_count 0.12916 0.11335 1.139 0.25506
## category_code_LT01_15_count -0.30135 0.76087 -0.396 0.69223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.625741081429574
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9656 -0.7781 0.0590 0.9538 3.7792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93535 0.09054 109.734 < 2e-16 ***
## category_code_LT01_1_count 0.28739 0.08829 3.255 0.00121 **
## category_code_LT01_4_count 0.82871 0.08432 9.829 < 2e-16 ***
## category_code_LT01_5_count 0.93106 0.06195 15.029 < 2e-16 ***
## category_code_LT01_6_count 0.45026 0.15214 2.960 0.00323 **
## category_code_LT01_10_count 0.11667 0.11303 1.032 0.30249
## category_code_LT01_16_count 1.46987 1.16086 1.266 0.20604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.63161435702078
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9979 -0.7630 0.0646 0.8797 3.6457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97015 0.08661 115.116 < 2e-16 ***
## category_code_LT01_1_count 0.23774 0.08925 2.664 0.00798 **
## category_code_LT01_4_count 0.69772 0.09485 7.356 8.01e-13 ***
## category_code_LT01_5_count 0.92741 0.06172 15.026 < 2e-16 ***
## category_code_LT01_6_count 0.38656 0.15121 2.556 0.01088 *
## category_code_LT01_11_count 0.37689 0.11534 3.268 0.00116 **
## category_code_LT01_12_count -0.12580 0.21098 -0.596 0.55129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6361, Adjusted R-squared: 0.6316
## F-statistic: 143 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.63134887443075
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9953 -0.7588 0.0697 0.8826 3.6608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.969847 0.086641 115.071 < 2e-16 ***
## category_code_LT01_1_count 0.233077 0.089799 2.596 0.00973 **
## category_code_LT01_4_count 0.698976 0.094994 7.358 7.9e-13 ***
## category_code_LT01_5_count 0.924169 0.061557 15.013 < 2e-16 ***
## category_code_LT01_6_count 0.379698 0.150868 2.517 0.01216 *
## category_code_LT01_11_count 0.360251 0.112107 3.213 0.00140 **
## category_code_LT01_13_count 0.009848 0.242737 0.041 0.96765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6313
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.631422396749665
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9950 -0.7569 0.0608 0.8863 3.6618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97107 0.08672 114.979 < 2e-16 ***
## category_code_LT01_1_count 0.23063 0.08948 2.577 0.01025 *
## category_code_LT01_4_count 0.69482 0.09585 7.249 1.64e-12 ***
## category_code_LT01_5_count 0.92191 0.06196 14.880 < 2e-16 ***
## category_code_LT01_6_count 0.38431 0.15155 2.536 0.01153 *
## category_code_LT01_11_count 0.35951 0.11205 3.208 0.00142 **
## category_code_LT01_14_count 0.10304 0.32650 0.316 0.75246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6314
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.631464717552564
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9945 -0.7571 0.0699 0.8825 3.6561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96914 0.08664 115.060 < 2e-16 ***
## category_code_LT01_1_count 0.23949 0.09025 2.654 0.00822 **
## category_code_LT01_4_count 0.70003 0.09486 7.379 6.84e-13 ***
## category_code_LT01_5_count 0.92406 0.06151 15.023 < 2e-16 ***
## category_code_LT01_6_count 0.38156 0.15087 2.529 0.01175 *
## category_code_LT01_11_count 0.36163 0.11205 3.227 0.00133 **
## category_code_LT01_15_count -0.29689 0.75171 -0.395 0.69305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6359, Adjusted R-squared: 0.6315
## F-statistic: 142.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.63236466240004
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9929 -0.7549 0.0755 0.8860 3.6649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96903 0.08652 115.219 < 2e-16 ***
## category_code_LT01_1_count 0.23539 0.08890 2.648 0.00836 **
## category_code_LT01_4_count 0.69382 0.09483 7.316 1.05e-12 ***
## category_code_LT01_5_count 0.92181 0.06147 14.997 < 2e-16 ***
## category_code_LT01_6_count 0.39167 0.15096 2.595 0.00975 **
## category_code_LT01_11_count 0.35309 0.11205 3.151 0.00172 **
## category_code_LT01_16_count 1.34046 1.15015 1.165 0.24440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 491 degrees of freedom
## Multiple R-squared: 0.6368, Adjusted R-squared: 0.6324
## F-statistic: 143.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.623622743255976
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9923 -0.7646 0.0439 0.9492 3.8865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96114 0.08750 113.840 < 2e-16 ***
## category_code_LT01_1_count 0.28026 0.09003 3.113 0.00196 **
## category_code_LT01_4_count 0.84459 0.08452 9.992 < 2e-16 ***
## category_code_LT01_5_count 0.93240 0.06241 14.941 < 2e-16 ***
## category_code_LT01_6_count 0.46286 0.15108 3.064 0.00231 **
## category_code_LT01_12_count 0.03884 0.20708 0.188 0.85131
## category_code_LT01_13_count 0.03923 0.24511 0.160 0.87290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6236
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.623714464586381
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9921 -0.7616 0.0480 0.9495 3.8860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96258 0.08758 113.751 < 2e-16 ***
## category_code_LT01_1_count 0.27882 0.08960 3.112 0.00197 **
## category_code_LT01_4_count 0.84015 0.08548 9.829 < 2e-16 ***
## category_code_LT01_5_count 0.93004 0.06277 14.818 < 2e-16 ***
## category_code_LT01_6_count 0.46840 0.15187 3.084 0.00216 **
## category_code_LT01_12_count 0.03329 0.20763 0.160 0.87269
## category_code_LT01_14_count 0.12608 0.33075 0.381 0.70322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.623670440489251
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9919 -0.7601 0.0536 0.9459 3.8825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96049 0.08751 113.818 < 2e-16 ***
## category_code_LT01_1_count 0.28701 0.09058 3.169 0.00163 **
## category_code_LT01_4_count 0.84682 0.08432 10.043 < 2e-16 ***
## category_code_LT01_5_count 0.93272 0.06236 14.957 < 2e-16 ***
## category_code_LT01_6_count 0.46428 0.15118 3.071 0.00225 **
## category_code_LT01_12_count 0.03674 0.20722 0.177 0.85934
## category_code_LT01_15_count -0.22526 0.76000 -0.296 0.76705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.624952652787887
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7645 0.0463 0.9576 3.8852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96032 0.08735 114.033 < 2e-16 ***
## category_code_LT01_1_count 0.28332 0.08900 3.183 0.00155 **
## category_code_LT01_4_count 0.83618 0.08440 9.908 < 2e-16 ***
## category_code_LT01_5_count 0.92981 0.06229 14.926 < 2e-16 ***
## category_code_LT01_6_count 0.47450 0.15105 3.141 0.00178 **
## category_code_LT01_12_count 0.03639 0.20670 0.176 0.86032
## category_code_LT01_16_count 1.54177 1.15992 1.329 0.18440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.623716874999607
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9926 -0.7613 0.0545 0.9518 3.8863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96274 0.08759 113.749 < 2e-16 ***
## category_code_LT01_1_count 0.27824 0.08996 3.093 0.00209 **
## category_code_LT01_4_count 0.84013 0.08545 9.832 < 2e-16 ***
## category_code_LT01_5_count 0.93047 0.06259 14.866 < 2e-16 ***
## category_code_LT01_6_count 0.47209 0.15069 3.133 0.00183 **
## category_code_LT01_13_count 0.04163 0.24509 0.170 0.86520
## category_code_LT01_14_count 0.13111 0.32984 0.398 0.69117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.623660620822157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9925 -0.7579 0.0388 0.9437 3.8823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96057 0.08752 113.813 < 2e-16 ***
## category_code_LT01_1_count 0.28710 0.09096 3.156 0.00170 **
## category_code_LT01_4_count 0.84739 0.08418 10.067 < 2e-16 ***
## category_code_LT01_5_count 0.93344 0.06213 15.025 < 2e-16 ***
## category_code_LT01_6_count 0.46791 0.15009 3.118 0.00193 **
## category_code_LT01_13_count 0.03358 0.24603 0.136 0.89150
## category_code_LT01_15_count -0.22172 0.76231 -0.291 0.77129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.624961730166566
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9902 -0.7585 0.0437 0.9621 3.8856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96043 0.08735 114.033 < 2e-16 ***
## category_code_LT01_1_count 0.28259 0.08925 3.166 0.00164 **
## category_code_LT01_4_count 0.83620 0.08426 9.923 < 2e-16 ***
## category_code_LT01_5_count 0.93033 0.06206 14.990 < 2e-16 ***
## category_code_LT01_6_count 0.47844 0.15000 3.190 0.00152 **
## category_code_LT01_13_count 0.05069 0.24478 0.207 0.83604
## category_code_LT01_16_count 1.55187 1.16047 1.337 0.18175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.623765129417426
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9921 -0.7613 0.0503 0.9468 3.8822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96205 0.08760 113.728 < 2e-16 ***
## category_code_LT01_1_count 0.28515 0.09039 3.155 0.00171 **
## category_code_LT01_4_count 0.84242 0.08521 9.887 < 2e-16 ***
## category_code_LT01_5_count 0.93078 0.06253 14.884 < 2e-16 ***
## category_code_LT01_6_count 0.47327 0.15075 3.139 0.00179 **
## category_code_LT01_14_count 0.12984 0.32977 0.394 0.69396
## category_code_LT01_15_count -0.23007 0.75924 -0.303 0.76200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6238
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.625095183303817
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7546 0.0507 0.9641 3.8852
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96218 0.08742 113.955 < 2e-16 ***
## category_code_LT01_1_count 0.28065 0.08888 3.158 0.00169 **
## category_code_LT01_4_count 0.83044 0.08537 9.728 < 2e-16 ***
## category_code_LT01_5_count 0.92724 0.06248 14.840 < 2e-16 ***
## category_code_LT01_6_count 0.48473 0.15069 3.217 0.00138 **
## category_code_LT01_14_count 0.15380 0.32965 0.467 0.64102
## category_code_LT01_16_count 1.57269 1.16127 1.354 0.17627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6251
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.624986561420473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7586 0.0495 0.9559 3.8814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95980 0.08736 114.008 < 2e-16 ***
## category_code_LT01_1_count 0.28947 0.08971 3.227 0.00134 **
## category_code_LT01_4_count 0.83868 0.08403 9.981 < 2e-16 ***
## category_code_LT01_5_count 0.93074 0.06201 15.008 < 2e-16 ***
## category_code_LT01_6_count 0.47927 0.15006 3.194 0.00149 **
## category_code_LT01_15_count -0.20819 0.75820 -0.275 0.78375
## category_code_LT01_16_count 1.53674 1.16010 1.325 0.18590
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.626848410776012
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0136 -0.7668 0.0199 0.8578 3.8747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97593 0.08702 114.634 < 2e-16 ***
## category_code_LT01_1_count 0.27592 0.08832 3.124 0.00189 **
## category_code_LT01_4_count 0.82906 0.08367 9.909 < 2e-16 ***
## category_code_LT01_5_count 0.94177 0.06238 15.097 < 2e-16 ***
## category_code_LT01_7_count 0.45094 0.15403 2.928 0.00357 **
## category_code_LT01_8_count -0.19982 0.27266 -0.733 0.46400
## category_code_LT01_9_count 0.43552 0.22382 1.946 0.05225 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.625404912495034
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9909 -0.7688 0.0316 0.8849 3.7277
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94748 0.09062 109.768 < 2e-16 ***
## category_code_LT01_1_count 0.28637 0.08840 3.239 0.00128 **
## category_code_LT01_4_count 0.83584 0.08382 9.971 < 2e-16 ***
## category_code_LT01_5_count 0.95003 0.06232 15.243 < 2e-16 ***
## category_code_LT01_7_count 0.46358 0.15409 3.009 0.00276 **
## category_code_LT01_8_count -0.19218 0.27313 -0.704 0.48199
## category_code_LT01_10_count 0.15309 0.11166 1.371 0.17101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.630826811841384
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0226 -0.7725 0.0255 0.8436 3.6525
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98630 0.08653 115.415 < 2e-16 ***
## category_code_LT01_1_count 0.23764 0.08909 2.667 0.00790 **
## category_code_LT01_4_count 0.71589 0.09391 7.623 1.29e-13 ***
## category_code_LT01_5_count 0.93976 0.06198 15.163 < 2e-16 ***
## category_code_LT01_7_count 0.36509 0.15723 2.322 0.02064 *
## category_code_LT01_8_count -0.15597 0.27125 -0.575 0.56555
## category_code_LT01_11_count 0.34387 0.11388 3.020 0.00266 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6308
## F-statistic: 142.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.624215594290056
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0228 -0.7709 0.0185 0.8726 3.8681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98131 0.08728 114.355 < 2e-16 ***
## category_code_LT01_1_count 0.27773 0.08927 3.111 0.00197 **
## category_code_LT01_4_count 0.84505 0.08402 10.057 < 2e-16 ***
## category_code_LT01_5_count 0.94732 0.06275 15.097 < 2e-16 ***
## category_code_LT01_7_count 0.48179 0.15373 3.134 0.00183 **
## category_code_LT01_8_count -0.18943 0.27365 -0.692 0.48913
## category_code_LT01_12_count 0.11620 0.20552 0.565 0.57208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6288, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.624034164229971
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0259 -0.7710 0.0075 0.8698 3.8611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98140 0.08731 114.328 < 2e-16 ***
## category_code_LT01_1_count 0.28759 0.08934 3.219 0.00137 **
## category_code_LT01_4_count 0.85287 0.08321 10.250 < 2e-16 ***
## category_code_LT01_5_count 0.95165 0.06248 15.231 < 2e-16 ***
## category_code_LT01_7_count 0.48707 0.15470 3.148 0.00174 **
## category_code_LT01_8_count -0.18956 0.27417 -0.691 0.48965
## category_code_LT01_13_count -0.07093 0.24686 -0.287 0.77399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.623977532727547
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0256 -0.7707 0.0090 0.8749 3.8632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98115 0.08743 114.161 < 2e-16 ***
## category_code_LT01_1_count 0.28498 0.08896 3.203 0.00145 **
## category_code_LT01_4_count 0.85291 0.08379 10.179 < 2e-16 ***
## category_code_LT01_5_count 0.95156 0.06277 15.158 < 2e-16 ***
## category_code_LT01_7_count 0.48306 0.15407 3.135 0.00182 **
## category_code_LT01_8_count -0.18409 0.27360 -0.673 0.50137
## category_code_LT01_14_count -0.03047 0.32875 -0.093 0.92618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.62397233350502
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7711 0.0105 0.8769 3.8629
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98150 0.08733 114.302 < 2e-16 ***
## category_code_LT01_1_count 0.28489 0.09004 3.164 0.00165 **
## category_code_LT01_4_count 0.85221 0.08337 10.223 < 2e-16 ***
## category_code_LT01_5_count 0.95096 0.06244 15.230 < 2e-16 ***
## category_code_LT01_7_count 0.48191 0.15392 3.131 0.00185 **
## category_code_LT01_8_count -0.18425 0.27360 -0.673 0.50098
## category_code_LT01_15_count -0.03224 0.75915 -0.042 0.96615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.625003124511615
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0244 -0.7708 0.0269 0.8689 3.8623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98149 0.08719 114.479 < 2e-16 ***
## category_code_LT01_1_count 0.28576 0.08845 3.231 0.00132 **
## category_code_LT01_4_count 0.84584 0.08321 10.165 < 2e-16 ***
## category_code_LT01_5_count 0.94940 0.06237 15.223 < 2e-16 ***
## category_code_LT01_7_count 0.48158 0.15357 3.136 0.00182 **
## category_code_LT01_8_count -0.20039 0.27356 -0.733 0.46421
## category_code_LT01_16_count 1.34758 1.15918 1.163 0.24559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.627412373787455
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9790 -0.7547 0.0261 0.8664 3.7645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94628 0.09036 110.074 < 2e-16 ***
## category_code_LT01_1_count 0.27647 0.08825 3.133 0.00183 **
## category_code_LT01_4_count 0.81784 0.08424 9.709 < 2e-16 ***
## category_code_LT01_5_count 0.93470 0.06164 15.163 < 2e-16 ***
## category_code_LT01_7_count 0.43472 0.15431 2.817 0.00504 **
## category_code_LT01_9_count 0.39938 0.22527 1.773 0.07686 .
## category_code_LT01_10_count 0.12698 0.11219 1.132 0.25825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.632820221817719
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7689 0.0373 0.8636 3.6723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97962 0.08631 115.621 < 2e-16 ***
## category_code_LT01_1_count 0.23027 0.08886 2.591 0.00984 **
## category_code_LT01_4_count 0.70041 0.09404 7.448 4.29e-13 ***
## category_code_LT01_5_count 0.92614 0.06127 15.116 < 2e-16 ***
## category_code_LT01_7_count 0.33857 0.15727 2.153 0.03182 *
## category_code_LT01_9_count 0.38522 0.22248 1.731 0.08399 .
## category_code_LT01_11_count 0.33234 0.11378 2.921 0.00365 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.365 on 491 degrees of freedom
## Multiple R-squared: 0.6373, Adjusted R-squared: 0.6328
## F-statistic: 143.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.626640672557767
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0046 -0.7635 0.0106 0.8539 3.8820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97395 0.08702 114.619 < 2e-16 ***
## category_code_LT01_1_count 0.26827 0.08905 3.013 0.00272 **
## category_code_LT01_4_count 0.82340 0.08454 9.740 < 2e-16 ***
## category_code_LT01_5_count 0.93158 0.06206 15.010 < 2e-16 ***
## category_code_LT01_7_count 0.44770 0.15402 2.907 0.00382 **
## category_code_LT01_9_count 0.42886 0.22382 1.916 0.05593 .
## category_code_LT01_12_count 0.10513 0.20477 0.513 0.60791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6266
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.626446796169234
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0073 -0.7646 0.0256 0.8470 3.8769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97415 0.08704 114.592 < 2e-16 ***
## category_code_LT01_1_count 0.27524 0.08914 3.088 0.00213 **
## category_code_LT01_4_count 0.82993 0.08381 9.902 < 2e-16 ***
## category_code_LT01_5_count 0.93520 0.06176 15.142 < 2e-16 ***
## category_code_LT01_7_count 0.44966 0.15510 2.899 0.00391 **
## category_code_LT01_9_count 0.42911 0.22453 1.911 0.05657 .
## category_code_LT01_13_count -0.02284 0.24628 -0.093 0.92613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.626473909909137
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0071 -0.7642 0.0191 0.8617 3.8775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97321 0.08716 114.418 < 2e-16 ***
## category_code_LT01_1_count 0.27585 0.08871 3.110 0.00198 **
## category_code_LT01_4_count 0.83158 0.08426 9.869 < 2e-16 ***
## category_code_LT01_5_count 0.93631 0.06204 15.092 < 2e-16 ***
## category_code_LT01_7_count 0.44979 0.15429 2.915 0.00372 **
## category_code_LT01_9_count 0.43344 0.22421 1.933 0.05379 .
## category_code_LT01_14_count -0.06903 0.32819 -0.210 0.83348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.626440275949939
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0072 -0.7645 0.0300 0.8493 3.8776
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.974194 0.087054 114.575 < 2e-16 ***
## category_code_LT01_1_count 0.274223 0.089831 3.053 0.00239 **
## category_code_LT01_4_count 0.829589 0.083937 9.884 < 2e-16 ***
## category_code_LT01_5_count 0.934996 0.061725 15.148 < 2e-16 ***
## category_code_LT01_7_count 0.447968 0.154190 2.905 0.00383 **
## category_code_LT01_9_count 0.430705 0.223903 1.924 0.05498 .
## category_code_LT01_15_count -0.004386 0.756815 -0.006 0.99538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.62726096024995
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7637 0.0391 0.8496 3.8767
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97415 0.08694 114.719 < 2e-16 ***
## category_code_LT01_1_count 0.27560 0.08825 3.123 0.00190 **
## category_code_LT01_4_count 0.82470 0.08375 9.847 < 2e-16 ***
## category_code_LT01_5_count 0.93336 0.06168 15.133 < 2e-16 ***
## category_code_LT01_7_count 0.44803 0.15389 2.911 0.00376 **
## category_code_LT01_9_count 0.41964 0.22386 1.875 0.06145 .
## category_code_LT01_16_count 1.20147 1.15553 1.040 0.29896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.631670939743065
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9869 -0.7538 0.0300 0.8609 3.5396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95497 0.08989 110.745 < 2e-16 ***
## category_code_LT01_1_count 0.23868 0.08897 2.683 0.00755 **
## category_code_LT01_4_count 0.70401 0.09425 7.470 3.71e-13 ***
## category_code_LT01_5_count 0.93350 0.06119 15.257 < 2e-16 ***
## category_code_LT01_7_count 0.34787 0.15737 2.211 0.02753 *
## category_code_LT01_10_count 0.13380 0.11086 1.207 0.22804
## category_code_LT01_11_count 0.33879 0.11384 2.976 0.00306 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6361, Adjusted R-squared: 0.6317
## F-statistic: 143.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.625197502054002
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9831 -0.7603 0.0096 0.8688 3.7387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94649 0.09063 109.747 < 2e-16 ***
## category_code_LT01_1_count 0.27905 0.08915 3.130 0.00185 **
## category_code_LT01_4_count 0.83078 0.08464 9.815 < 2e-16 ***
## category_code_LT01_5_count 0.94026 0.06199 15.167 < 2e-16 ***
## category_code_LT01_7_count 0.46056 0.15407 2.989 0.00294 **
## category_code_LT01_10_count 0.14864 0.11183 1.329 0.18440
## category_code_LT01_12_count 0.09704 0.20543 0.472 0.63688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6297, Adjusted R-squared: 0.6252
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.625084806821723
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9849 -0.7599 0.0390 0.8603 3.7295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94576 0.09065 109.714 < 2e-16 ***
## category_code_LT01_1_count 0.28773 0.08917 3.227 0.00134 **
## category_code_LT01_4_count 0.83705 0.08391 9.976 < 2e-16 ***
## category_code_LT01_5_count 0.94392 0.06166 15.307 < 2e-16 ***
## category_code_LT01_7_count 0.46509 0.15495 3.001 0.00282 **
## category_code_LT01_10_count 0.15217 0.11172 1.362 0.17379
## category_code_LT01_13_count -0.06759 0.24605 -0.275 0.78365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.6251
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.625147230179541
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9827 -0.7522 0.0185 0.8950 3.7231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94212 0.09119 109.022 < 2e-16 ***
## category_code_LT01_1_count 0.28810 0.08885 3.242 0.00127 **
## category_code_LT01_4_count 0.83942 0.08423 9.966 < 2e-16 ***
## category_code_LT01_5_count 0.94602 0.06198 15.264 < 2e-16 ***
## category_code_LT01_7_count 0.46326 0.15423 3.004 0.00280 **
## category_code_LT01_10_count 0.16107 0.11428 1.409 0.15937
## category_code_LT01_14_count -0.13320 0.33589 -0.397 0.69188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6297, Adjusted R-squared: 0.6251
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.625049626155829
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.984 -0.765 0.027 0.862 3.729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94546 0.09073 109.617 < 2e-16 ***
## category_code_LT01_1_count 0.28735 0.08993 3.195 0.00149 **
## category_code_LT01_4_count 0.83708 0.08400 9.965 < 2e-16 ***
## category_code_LT01_5_count 0.94343 0.06164 15.305 < 2e-16 ***
## category_code_LT01_7_count 0.45927 0.15430 2.976 0.00306 **
## category_code_LT01_10_count 0.15319 0.11216 1.366 0.17261
## category_code_LT01_15_count -0.13050 0.76121 -0.171 0.86395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6296, Adjusted R-squared: 0.625
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.625885531472488
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9848 -0.7623 0.0340 0.8708 3.7368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94725 0.09055 109.853 < 2e-16 ***
## category_code_LT01_1_count 0.28573 0.08832 3.235 0.00130 **
## category_code_LT01_4_count 0.83131 0.08390 9.909 < 2e-16 ***
## category_code_LT01_5_count 0.94155 0.06160 15.285 < 2e-16 ***
## category_code_LT01_7_count 0.46055 0.15393 2.992 0.00291 **
## category_code_LT01_10_count 0.14547 0.11171 1.302 0.19343
## category_code_LT01_16_count 1.22887 1.15781 1.061 0.28904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.630628158626215
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0188 -0.7733 0.0266 0.8460 3.6464
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98520 0.08652 115.407 < 2e-16 ***
## category_code_LT01_1_count 0.23784 0.08939 2.661 0.00805 **
## category_code_LT01_4_count 0.71526 0.09393 7.615 1.36e-13 ***
## category_code_LT01_5_count 0.93588 0.06156 15.202 < 2e-16 ***
## category_code_LT01_7_count 0.35929 0.15746 2.282 0.02293 *
## category_code_LT01_11_count 0.35409 0.11796 3.002 0.00282 **
## category_code_LT01_12_count -0.05437 0.21101 -0.258 0.79677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6351, Adjusted R-squared: 0.6306
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.630632267207499
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0176 -0.7743 0.0265 0.8613 3.6514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98474 0.08652 115.406 < 2e-16 ***
## category_code_LT01_1_count 0.23892 0.08979 2.661 0.00805 **
## category_code_LT01_4_count 0.71593 0.09397 7.619 1.33e-13 ***
## category_code_LT01_5_count 0.93480 0.06129 15.251 < 2e-16 ***
## category_code_LT01_7_count 0.36610 0.15800 2.317 0.02091 *
## category_code_LT01_11_count 0.34638 0.11384 3.043 0.00247 **
## category_code_LT01_13_count -0.06545 0.24416 -0.268 0.78877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6351, Adjusted R-squared: 0.6306
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.630582830282254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0174 -0.7705 0.0299 0.8489 3.6535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98458 0.08664 115.242 < 2e-16 ***
## category_code_LT01_1_count 0.23653 0.08947 2.644 0.00847 **
## category_code_LT01_4_count 0.71601 0.09452 7.575 1.8e-13 ***
## category_code_LT01_5_count 0.93484 0.06162 15.171 < 2e-16 ***
## category_code_LT01_7_count 0.36252 0.15747 2.302 0.02174 *
## category_code_LT01_11_count 0.34607 0.11385 3.040 0.00249 **
## category_code_LT01_14_count -0.02553 0.32585 -0.078 0.93758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.635, Adjusted R-squared: 0.6306
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.63060452409241
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0171 -0.7742 0.0272 0.8466 3.6511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98465 0.08653 115.387 < 2e-16 ***
## category_code_LT01_1_count 0.23875 0.09039 2.641 0.00852 **
## category_code_LT01_4_count 0.71589 0.09401 7.615 1.36e-13 ***
## category_code_LT01_5_count 0.93431 0.06127 15.249 < 2e-16 ***
## category_code_LT01_7_count 0.36021 0.15739 2.289 0.02253 *
## category_code_LT01_11_count 0.34711 0.11396 3.046 0.00245 **
## category_code_LT01_15_count -0.14086 0.75320 -0.187 0.85173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6351, Adjusted R-squared: 0.6306
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.631331607304498
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0160 -0.7633 0.0432 0.8406 3.6560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98467 0.08643 115.518 < 2e-16 ***
## category_code_LT01_1_count 0.23775 0.08900 2.671 0.00780 **
## category_code_LT01_4_count 0.71199 0.09389 7.583 1.7e-13 ***
## category_code_LT01_5_count 0.93268 0.06123 15.232 < 2e-16 ***
## category_code_LT01_7_count 0.36274 0.15702 2.310 0.02129 *
## category_code_LT01_11_count 0.34104 0.11384 2.996 0.00288 **
## category_code_LT01_16_count 1.15099 1.14904 1.002 0.31699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6313
## F-statistic: 142.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.623895955974716
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0168 -0.7690 0.0186 0.8642 3.8690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97944 0.08729 114.325 < 2e-16 ***
## category_code_LT01_1_count 0.27906 0.09001 3.100 0.00205 **
## category_code_LT01_4_count 0.84631 0.08411 10.062 < 2e-16 ***
## category_code_LT01_5_count 0.94137 0.06212 15.153 < 2e-16 ***
## category_code_LT01_7_count 0.48282 0.15463 3.122 0.00190 **
## category_code_LT01_12_count 0.11208 0.20551 0.545 0.58575
## category_code_LT01_13_count -0.06109 0.24638 -0.248 0.80428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6284, Adjusted R-squared: 0.6239
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.623862596379129
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0167 -0.7686 0.0133 0.8656 3.8707
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97901 0.08741 114.160 < 2e-16 ***
## category_code_LT01_1_count 0.27725 0.08962 3.094 0.00209 **
## category_code_LT01_4_count 0.84683 0.08460 10.010 < 2e-16 ***
## category_code_LT01_5_count 0.94177 0.06241 15.091 < 2e-16 ***
## category_code_LT01_7_count 0.47996 0.15403 3.116 0.00194 **
## category_code_LT01_12_count 0.11326 0.20592 0.550 0.58255
## category_code_LT01_14_count -0.04411 0.32945 -0.134 0.89355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6284, Adjusted R-squared: 0.6239
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.623849440730674
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0167 -0.7691 0.0146 0.8685 3.8705
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97958 0.08731 114.305 < 2e-16 ***
## category_code_LT01_1_count 0.27667 0.09083 3.046 0.00244 **
## category_code_LT01_4_count 0.84573 0.08430 10.032 < 2e-16 ***
## category_code_LT01_5_count 0.94096 0.06210 15.151 < 2e-16 ***
## category_code_LT01_7_count 0.47852 0.15388 3.110 0.00198 **
## category_code_LT01_12_count 0.11131 0.20565 0.541 0.58857
## category_code_LT01_15_count -0.02084 0.75976 -0.027 0.97813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6284, Adjusted R-squared: 0.6238
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.624815791609424
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7690 0.0242 0.8728 3.8700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97941 0.08718 114.469 < 2e-16 ***
## category_code_LT01_1_count 0.27761 0.08918 3.113 0.00196 **
## category_code_LT01_4_count 0.83970 0.08411 9.983 < 2e-16 ***
## category_code_LT01_5_count 0.93893 0.06205 15.132 < 2e-16 ***
## category_code_LT01_7_count 0.47786 0.15355 3.112 0.00197 **
## category_code_LT01_12_count 0.11075 0.20525 0.540 0.58974
## category_code_LT01_16_count 1.30264 1.15800 1.125 0.26118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.62367653008688
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0197 -0.7688 0.0126 0.8686 3.8643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97926 0.08743 114.134 < 2e-16 ***
## category_code_LT01_1_count 0.28618 0.08974 3.189 0.00152 **
## category_code_LT01_4_count 0.85404 0.08390 10.180 < 2e-16 ***
## category_code_LT01_5_count 0.94574 0.06213 15.222 < 2e-16 ***
## category_code_LT01_7_count 0.48422 0.15498 3.124 0.00189 **
## category_code_LT01_13_count -0.06018 0.24650 -0.244 0.80721
## category_code_LT01_14_count -0.03443 0.32895 -0.105 0.91669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.62367168152278
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.7692 0.0143 0.8686 3.8637
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97963 0.08733 114.274 < 2e-16 ***
## category_code_LT01_1_count 0.28645 0.09098 3.148 0.00174 **
## category_code_LT01_4_count 0.85337 0.08348 10.222 < 2e-16 ***
## category_code_LT01_5_count 0.94506 0.06178 15.298 < 2e-16 ***
## category_code_LT01_7_count 0.48284 0.15476 3.120 0.00192 **
## category_code_LT01_13_count -0.06104 0.24733 -0.247 0.80518
## category_code_LT01_15_count -0.05184 0.76219 -0.068 0.94580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.624625730764346
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0180 -0.7691 0.0204 0.8594 3.8639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97955 0.08720 114.440 < 2e-16 ***
## category_code_LT01_1_count 0.28620 0.08921 3.208 0.00142 **
## category_code_LT01_4_count 0.84697 0.08331 10.166 < 2e-16 ***
## category_code_LT01_5_count 0.94295 0.06173 15.277 < 2e-16 ***
## category_code_LT01_7_count 0.48175 0.15448 3.118 0.00192 **
## category_code_LT01_13_count -0.05070 0.24625 -0.206 0.83697
## category_code_LT01_16_count 1.29701 1.15889 1.119 0.26361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.623632578245055
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0195 -0.7690 0.0150 0.8687 3.8656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97939 0.08745 114.115 < 2e-16 ***
## category_code_LT01_1_count 0.28408 0.09045 3.141 0.00179 **
## category_code_LT01_4_count 0.85349 0.08404 10.156 < 2e-16 ***
## category_code_LT01_5_count 0.94527 0.06210 15.221 < 2e-16 ***
## category_code_LT01_7_count 0.47979 0.15421 3.111 0.00197 **
## category_code_LT01_14_count -0.03269 0.32889 -0.099 0.92087
## category_code_LT01_15_count -0.03616 0.75948 -0.048 0.96204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6236
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.624594951244652
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7691 0.0205 0.8610 3.8654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97950 0.08732 114.282 < 2e-16 ***
## category_code_LT01_1_count 0.28421 0.08886 3.199 0.00147 **
## category_code_LT01_4_count 0.84676 0.08392 10.090 < 2e-16 ***
## category_code_LT01_5_count 0.94287 0.06206 15.193 < 2e-16 ***
## category_code_LT01_7_count 0.47876 0.15388 3.111 0.00197 **
## category_code_LT01_14_count -0.01517 0.32884 -0.046 0.96323
## category_code_LT01_16_count 1.30219 1.15963 1.123 0.26201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.624593613229289
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7692 0.0208 0.8615 3.8653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97967 0.08722 114.421 < 2e-16 ***
## category_code_LT01_1_count 0.28414 0.08993 3.159 0.00168 **
## category_code_LT01_4_count 0.84639 0.08347 10.140 < 2e-16 ***
## category_code_LT01_5_count 0.94257 0.06170 15.277 < 2e-16 ***
## category_code_LT01_7_count 0.47819 0.15373 3.111 0.00198 **
## category_code_LT01_15_count -0.01474 0.75874 -0.019 0.98451
## category_code_LT01_16_count 1.30416 1.15869 1.126 0.26091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.621744753857529
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9843 -0.7768 0.0121 0.8834 3.7253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94018 0.09103 109.202 < 2e-16 ***
## category_code_LT01_1_count 0.30127 0.08858 3.401 0.000725 ***
## category_code_LT01_4_count 0.89356 0.08043 11.110 < 2e-16 ***
## category_code_LT01_5_count 0.95111 0.06271 15.166 < 2e-16 ***
## category_code_LT01_8_count -0.18628 0.27447 -0.679 0.497650
## category_code_LT01_9_count 0.46396 0.22605 2.052 0.040658 *
## category_code_LT01_10_count 0.15251 0.11273 1.353 0.176742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.629587399726466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0148 -0.7699 0.0504 0.9090 3.6250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98030 0.08672 115.084 < 2e-16 ***
## category_code_LT01_1_count 0.24096 0.08920 2.701 0.007149 **
## category_code_LT01_4_count 0.73626 0.09304 7.914 1.68e-14 ***
## category_code_LT01_5_count 0.93727 0.06219 15.070 < 2e-16 ***
## category_code_LT01_8_count -0.15098 0.27168 -0.556 0.578640
## category_code_LT01_9_count 0.43030 0.22278 1.932 0.053991 .
## category_code_LT01_11_count 0.38887 0.11104 3.502 0.000504 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6341, Adjusted R-squared: 0.6296
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.620561903931516
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0154 -0.7853 0.0245 0.9207 3.8654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97335 0.08775 113.655 < 2e-16 ***
## category_code_LT01_1_count 0.29301 0.08944 3.276 0.00113 **
## category_code_LT01_4_count 0.90391 0.08050 11.229 < 2e-16 ***
## category_code_LT01_5_count 0.94811 0.06314 15.017 < 2e-16 ***
## category_code_LT01_8_count -0.18392 0.27500 -0.669 0.50394
## category_code_LT01_9_count 0.50194 0.22450 2.236 0.02581 *
## category_code_LT01_12_count 0.11194 0.20654 0.542 0.58809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6251, Adjusted R-squared: 0.6206
## F-statistic: 136.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.620366466120934
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0177 -0.7884 0.0254 0.9086 3.8626
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97368 0.08777 113.629 < 2e-16 ***
## category_code_LT01_1_count 0.29658 0.08968 3.307 0.00101 **
## category_code_LT01_4_count 0.90902 0.07993 11.373 < 2e-16 ***
## category_code_LT01_5_count 0.95095 0.06291 15.116 < 2e-16 ***
## category_code_LT01_8_count -0.17568 0.27543 -0.638 0.52387
## category_code_LT01_9_count 0.50677 0.22499 2.252 0.02474 *
## category_code_LT01_13_count 0.04991 0.24708 0.202 0.83999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6249, Adjusted R-squared: 0.6204
## F-statistic: 136.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.620336391520324
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0180 -0.7899 0.0238 0.9054 3.8608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97337 0.08791 113.451 < 2e-16 ***
## category_code_LT01_1_count 0.29960 0.08915 3.361 0.000838 ***
## category_code_LT01_4_count 0.91100 0.08038 11.334 < 2e-16 ***
## category_code_LT01_5_count 0.95187 0.06316 15.071 < 2e-16 ***
## category_code_LT01_8_count -0.17895 0.27495 -0.651 0.515435
## category_code_LT01_9_count 0.50448 0.22498 2.242 0.025389 *
## category_code_LT01_14_count -0.01445 0.33039 -0.044 0.965137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6249, Adjusted R-squared: 0.6203
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.620345693682142
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0179 -0.7888 0.0267 0.9060 3.8598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97340 0.08779 113.607 < 2e-16 ***
## category_code_LT01_1_count 0.30112 0.09017 3.339 0.000904 ***
## category_code_LT01_4_count 0.91111 0.07975 11.424 < 2e-16 ***
## category_code_LT01_5_count 0.95158 0.06283 15.146 < 2e-16 ***
## category_code_LT01_8_count -0.17885 0.27494 -0.651 0.515660
## category_code_LT01_9_count 0.50316 0.22461 2.240 0.025528 *
## category_code_LT01_15_count -0.09002 0.76236 -0.118 0.906053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6249, Adjusted R-squared: 0.6203
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.621208868339909
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0173 -0.7878 0.0262 0.9019 3.8597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97365 0.08768 113.756 < 2e-16 ***
## category_code_LT01_1_count 0.30088 0.08864 3.394 0.000743 ***
## category_code_LT01_4_count 0.90550 0.07964 11.370 < 2e-16 ***
## category_code_LT01_5_count 0.95039 0.06277 15.141 < 2e-16 ***
## category_code_LT01_8_count -0.19345 0.27495 -0.704 0.482026
## category_code_LT01_9_count 0.49277 0.22452 2.195 0.028646 *
## category_code_LT01_16_count 1.24133 1.16628 1.064 0.287692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6258, Adjusted R-squared: 0.6212
## F-statistic: 136.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.628212337155298
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9920 -0.7590 0.0425 0.8957 3.4723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95185 0.09031 110.196 < 2e-16 ***
## category_code_LT01_1_count 0.25061 0.08933 2.806 0.005222 **
## category_code_LT01_4_count 0.74100 0.09327 7.945 1.34e-14 ***
## category_code_LT01_5_count 0.94537 0.06214 15.214 < 2e-16 ***
## category_code_LT01_8_count -0.14233 0.27211 -0.523 0.601161
## category_code_LT01_10_count 0.15321 0.11112 1.379 0.168583
## category_code_LT01_11_count 0.39774 0.11105 3.581 0.000376 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6327, Adjusted R-squared: 0.6282
## F-statistic: 141 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.618686718627468
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9889 -0.7743 0.0348 0.9068 3.6921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93979 0.09140 108.756 < 2e-16 ***
## category_code_LT01_1_count 0.30645 0.08953 3.423 0.000672 ***
## category_code_LT01_4_count 0.91468 0.08053 11.358 < 2e-16 ***
## category_code_LT01_5_count 0.95830 0.06310 15.188 < 2e-16 ***
## category_code_LT01_8_count -0.17411 0.27560 -0.632 0.527848
## category_code_LT01_10_count 0.17979 0.11238 1.600 0.110254
## category_code_LT01_12_count 0.10176 0.20731 0.491 0.623734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6233, Adjusted R-squared: 0.6187
## F-statistic: 135.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.618499589715053
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9907 -0.7709 0.0301 0.9005 3.6854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9393795 0.0914267 108.714 < 2e-16 ***
## category_code_LT01_1_count 0.3121369 0.0897125 3.479 0.000547 ***
## category_code_LT01_4_count 0.9204194 0.0799130 11.518 < 2e-16 ***
## category_code_LT01_5_count 0.9614665 0.0628420 15.300 < 2e-16 ***
## category_code_LT01_8_count -0.1697787 0.2760904 -0.615 0.538880
## category_code_LT01_10_count 0.1826904 0.1123155 1.627 0.104467
## category_code_LT01_13_count 0.0003028 0.2473249 0.001 0.999024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6231, Adjusted R-squared: 0.6185
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.618552457712708
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9892 -0.7755 0.0239 0.8923 3.6793
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93669 0.09199 108.024 < 2e-16 ***
## category_code_LT01_1_count 0.31461 0.08929 3.524 0.000466 ***
## category_code_LT01_4_count 0.92285 0.08023 11.503 < 2e-16 ***
## category_code_LT01_5_count 0.96321 0.06313 15.258 < 2e-16 ***
## category_code_LT01_8_count -0.16933 0.27552 -0.615 0.539118
## category_code_LT01_10_count 0.18919 0.11497 1.646 0.100487
## category_code_LT01_14_count -0.08831 0.33853 -0.261 0.794302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6232, Adjusted R-squared: 0.6186
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.618578477038816
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9894 -0.7703 0.0250 0.8946 3.6797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93817 0.09148 108.633 < 2e-16 ***
## category_code_LT01_1_count 0.31729 0.09024 3.516 0.000478 ***
## category_code_LT01_4_count 0.92152 0.07976 11.554 < 2e-16 ***
## category_code_LT01_5_count 0.96138 0.06277 15.315 < 2e-16 ***
## category_code_LT01_8_count -0.16947 0.27550 -0.615 0.538767
## category_code_LT01_10_count 0.18581 0.11266 1.649 0.099733 .
## category_code_LT01_15_count -0.24434 0.76675 -0.319 0.750110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6232, Adjusted R-squared: 0.6186
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.619412786215699
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9910 -0.7677 0.0352 0.9082 3.6898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94064 0.09131 108.864 < 2e-16 ***
## category_code_LT01_1_count 0.31354 0.08870 3.535 0.000446 ***
## category_code_LT01_4_count 0.91533 0.07974 11.479 < 2e-16 ***
## category_code_LT01_5_count 0.96004 0.06272 15.307 < 2e-16 ***
## category_code_LT01_8_count -0.18460 0.27554 -0.670 0.503195
## category_code_LT01_10_count 0.17667 0.11225 1.574 0.116151
## category_code_LT01_16_count 1.26908 1.16921 1.085 0.278270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626880034664854
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0286 -0.7698 0.0290 0.8885 3.5906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98642 0.08699 114.798 < 2e-16 ***
## category_code_LT01_1_count 0.25046 0.08976 2.790 0.005469 **
## category_code_LT01_4_count 0.75515 0.09285 8.133 3.44e-15 ***
## category_code_LT01_5_count 0.94835 0.06248 15.178 < 2e-16 ***
## category_code_LT01_8_count -0.12856 0.27274 -0.471 0.637601
## category_code_LT01_11_count 0.42014 0.11478 3.660 0.000279 ***
## category_code_LT01_12_count -0.07958 0.21187 -0.376 0.707361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626775663611165
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7663 0.0248 0.8990 3.5999
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98604 0.08700 114.781 < 2e-16 ***
## category_code_LT01_1_count 0.24846 0.09028 2.752 0.006143 **
## category_code_LT01_4_count 0.75575 0.09298 8.128 3.57e-15 ***
## category_code_LT01_5_count 0.94646 0.06231 15.189 < 2e-16 ***
## category_code_LT01_8_count -0.13381 0.27307 -0.490 0.624343
## category_code_LT01_11_count 0.40931 0.11102 3.687 0.000252 ***
## category_code_LT01_13_count -0.01497 0.24461 -0.061 0.951227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626776302682068
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7666 0.0276 0.9019 3.6007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98639 0.08712 114.629 < 2e-16 ***
## category_code_LT01_1_count 0.24713 0.08990 2.749 0.006201 **
## category_code_LT01_4_count 0.75467 0.09358 8.064 5.67e-15 ***
## category_code_LT01_5_count 0.94585 0.06261 15.108 < 2e-16 ***
## category_code_LT01_8_count -0.13299 0.27256 -0.488 0.625825
## category_code_LT01_11_count 0.40902 0.11097 3.686 0.000253 ***
## category_code_LT01_14_count 0.02214 0.32693 0.068 0.946037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.626842898768366
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0263 -0.7659 0.0244 0.8947 3.5970
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98561 0.08700 114.771 < 2e-16 ***
## category_code_LT01_1_count 0.25235 0.09075 2.781 0.005635 **
## category_code_LT01_4_count 0.75634 0.09289 8.142 3.23e-15 ***
## category_code_LT01_5_count 0.94620 0.06225 15.200 < 2e-16 ***
## category_code_LT01_8_count -0.13222 0.27253 -0.485 0.627787
## category_code_LT01_11_count 0.41023 0.11102 3.695 0.000244 ***
## category_code_LT01_15_count -0.22957 0.75598 -0.304 0.761510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.627544987236861
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0259 -0.7660 0.0338 0.8938 3.6028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98593 0.08691 114.901 < 2e-16 ***
## category_code_LT01_1_count 0.24983 0.08941 2.794 0.005403 **
## category_code_LT01_4_count 0.75243 0.09281 8.107 4.16e-15 ***
## category_code_LT01_5_count 0.94515 0.06220 15.195 < 2e-16 ***
## category_code_LT01_8_count -0.14703 0.27263 -0.539 0.589917
## category_code_LT01_11_count 0.40395 0.11097 3.640 0.000301 ***
## category_code_LT01_16_count 1.16679 1.15646 1.009 0.313506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.632, Adjusted R-squared: 0.6275
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616700677825279
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0295 -0.7693 0.0116 0.9289 3.8517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97976 0.08815 113.210 < 2e-16 ***
## category_code_LT01_1_count 0.30351 0.09064 3.349 0.000875 ***
## category_code_LT01_4_count 0.93641 0.07979 11.735 < 2e-16 ***
## category_code_LT01_5_count 0.95925 0.06332 15.150 < 2e-16 ***
## category_code_LT01_8_count -0.16365 0.27681 -0.591 0.554649
## category_code_LT01_12_count 0.11906 0.20758 0.574 0.566524
## category_code_LT01_13_count 0.01231 0.24778 0.050 0.960386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616701672571171
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0296 -0.7696 0.0128 0.9288 3.8514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98001 0.08828 113.055 < 2e-16 ***
## category_code_LT01_1_count 0.30363 0.09014 3.369 0.000815 ***
## category_code_LT01_4_count 0.93603 0.08037 11.647 < 2e-16 ***
## category_code_LT01_5_count 0.95900 0.06357 15.084 < 2e-16 ***
## category_code_LT01_8_count -0.16468 0.27627 -0.596 0.551386
## category_code_LT01_12_count 0.11840 0.20798 0.569 0.569426
## category_code_LT01_14_count 0.02031 0.33197 0.061 0.951236
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616717472794928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0294 -0.7690 0.0113 0.9290 3.8498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97948 0.08816 113.193 < 2e-16 ***
## category_code_LT01_1_count 0.30669 0.09125 3.361 0.000837 ***
## category_code_LT01_4_count 0.93752 0.07971 11.761 < 2e-16 ***
## category_code_LT01_5_count 0.95940 0.06325 15.167 < 2e-16 ***
## category_code_LT01_8_count -0.16422 0.27626 -0.594 0.552484
## category_code_LT01_12_count 0.11802 0.20770 0.568 0.570161
## category_code_LT01_15_count -0.11867 0.76625 -0.155 0.876988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617748360092492
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0284 -0.7692 0.0178 0.9315 3.8503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97965 0.08803 113.367 < 2e-16 ***
## category_code_LT01_1_count 0.30570 0.08964 3.410 0.000702 ***
## category_code_LT01_4_count 0.93046 0.07962 11.686 < 2e-16 ***
## category_code_LT01_5_count 0.95781 0.06318 15.159 < 2e-16 ***
## category_code_LT01_8_count -0.18072 0.27623 -0.654 0.513254
## category_code_LT01_12_count 0.11880 0.20728 0.573 0.566821
## category_code_LT01_16_count 1.35891 1.17034 1.161 0.246154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6177
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616451397383926
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0324 -0.7647 -0.0049 0.9173 3.8471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98049 0.08830 113.024 < 2e-16 ***
## category_code_LT01_1_count 0.30915 0.09042 3.419 0.00068 ***
## category_code_LT01_4_count 0.94232 0.07981 11.808 < 2e-16 ***
## category_code_LT01_5_count 0.96230 0.06337 15.185 < 2e-16 ***
## category_code_LT01_8_count -0.15856 0.27676 -0.573 0.56695
## category_code_LT01_13_count 0.01462 0.24787 0.059 0.95298
## category_code_LT01_14_count 0.03256 0.33145 0.098 0.92180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616466835185189
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0321 -0.7601 0.0021 0.9180 3.8452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97974 0.08819 113.156 < 2e-16 ***
## category_code_LT01_1_count 0.31301 0.09158 3.418 0.000683 ***
## category_code_LT01_4_count 0.94443 0.07903 11.951 < 2e-16 ***
## category_code_LT01_5_count 0.96298 0.06300 15.285 < 2e-16 ***
## category_code_LT01_8_count -0.15827 0.27673 -0.572 0.567647
## category_code_LT01_13_count 0.01047 0.24882 0.042 0.966458
## category_code_LT01_15_count -0.13188 0.76902 -0.171 0.863903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617499057058243
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0312 -0.7628 0.0026 0.9186 3.8462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97997 0.08806 113.332 < 2e-16 ***
## category_code_LT01_1_count 0.31117 0.08983 3.464 0.000578 ***
## category_code_LT01_4_count 0.93700 0.07894 11.869 < 2e-16 ***
## category_code_LT01_5_count 0.96128 0.06293 15.274 < 2e-16 ***
## category_code_LT01_8_count -0.17397 0.27669 -0.629 0.529799
## category_code_LT01_13_count 0.02248 0.24760 0.091 0.927707
## category_code_LT01_16_count 1.36307 1.17119 1.164 0.245057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.61647267930466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0322 -0.7656 0.0026 0.9258 3.8450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98016 0.08831 113.008 < 2e-16 ***
## category_code_LT01_1_count 0.31273 0.09092 3.440 0.000632 ***
## category_code_LT01_4_count 0.94354 0.07967 11.843 < 2e-16 ***
## category_code_LT01_5_count 0.96245 0.06330 15.205 < 2e-16 ***
## category_code_LT01_8_count -0.15930 0.27621 -0.577 0.564380
## category_code_LT01_14_count 0.03188 0.33141 0.096 0.923411
## category_code_LT01_15_count -0.13427 0.76599 -0.175 0.860926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617510847594489
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0313 -0.7662 0.0058 0.9270 3.8457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98063 0.08818 113.186 < 2e-16 ***
## category_code_LT01_1_count 0.31096 0.08937 3.480 0.000547 ***
## category_code_LT01_4_count 0.93570 0.07965 11.748 < 2e-16 ***
## category_code_LT01_5_count 0.96050 0.06324 15.189 < 2e-16 ***
## category_code_LT01_8_count -0.17610 0.27619 -0.638 0.524036
## category_code_LT01_14_count 0.05066 0.33133 0.153 0.878546
## category_code_LT01_16_count 1.36856 1.17202 1.168 0.243498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617509443115048
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7627 0.0061 0.9271 3.8441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97968 0.08807 113.314 < 2e-16 ***
## category_code_LT01_1_count 0.31468 0.09034 3.483 0.00054 ***
## category_code_LT01_4_count 0.93831 0.07881 11.905 < 2e-16 ***
## category_code_LT01_5_count 0.96152 0.06287 15.295 < 2e-16 ***
## category_code_LT01_8_count -0.17522 0.27617 -0.634 0.52607
## category_code_LT01_15_count -0.11239 0.76518 -0.147 0.88329
## category_code_LT01_16_count 1.35570 1.17107 1.158 0.24757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.630331257085271
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7522 0.0313 0.9366 3.5180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95100 0.09004 110.523 < 2e-16 ***
## category_code_LT01_1_count 0.24204 0.08909 2.717 0.006827 **
## category_code_LT01_4_count 0.72480 0.09340 7.760 4.95e-14 ***
## category_code_LT01_5_count 0.93171 0.06142 15.170 < 2e-16 ***
## category_code_LT01_9_count 0.39405 0.22418 1.758 0.079419 .
## category_code_LT01_10_count 0.12717 0.11164 1.139 0.255231
## category_code_LT01_11_count 0.38284 0.11109 3.446 0.000617 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6348, Adjusted R-squared: 0.6303
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.621547851095603
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9769 -0.7657 0.0202 0.9071 3.7357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93927 0.09103 109.183 < 2e-16 ***
## category_code_LT01_1_count 0.29419 0.08931 3.294 0.00106 **
## category_code_LT01_4_count 0.88847 0.08127 10.932 < 2e-16 ***
## category_code_LT01_5_count 0.94172 0.06238 15.096 < 2e-16 ***
## category_code_LT01_9_count 0.45845 0.22603 2.028 0.04307 *
## category_code_LT01_10_count 0.14838 0.11289 1.314 0.18933
## category_code_LT01_12_count 0.09345 0.20644 0.453 0.65097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6215
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.621416340403924
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9786 -0.7694 0.0163 0.8984 3.7318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93922 0.09106 109.149 < 2e-16 ***
## category_code_LT01_1_count 0.29704 0.08952 3.318 0.000973 ***
## category_code_LT01_4_count 0.89243 0.08075 11.052 < 2e-16 ***
## category_code_LT01_5_count 0.94427 0.06209 15.208 < 2e-16 ***
## category_code_LT01_9_count 0.46244 0.22662 2.041 0.041828 *
## category_code_LT01_10_count 0.15014 0.11286 1.330 0.184049
## category_code_LT01_13_count 0.04565 0.24650 0.185 0.853155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6214
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.621478819780439
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9767 -0.7761 0.0117 0.9106 3.7217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93544 0.09161 108.451 < 2e-16 ***
## category_code_LT01_1_count 0.30257 0.08904 3.398 0.000734 ***
## category_code_LT01_4_count 0.89668 0.08093 11.080 < 2e-16 ***
## category_code_LT01_5_count 0.94696 0.06237 15.182 < 2e-16 ***
## category_code_LT01_9_count 0.46224 0.22618 2.044 0.041520 *
## category_code_LT01_10_count 0.15927 0.11534 1.381 0.167946
## category_code_LT01_14_count -0.11460 0.33744 -0.340 0.734282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6215
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.62143635876956
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9777 -0.7697 0.0215 0.8938 3.7249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93799 0.09112 109.062 < 2e-16 ***
## category_code_LT01_1_count 0.30344 0.09005 3.369 0.000812 ***
## category_code_LT01_4_count 0.89466 0.08055 11.106 < 2e-16 ***
## category_code_LT01_5_count 0.94473 0.06203 15.230 < 2e-16 ***
## category_code_LT01_9_count 0.45736 0.22621 2.022 0.043733 *
## category_code_LT01_10_count 0.15355 0.11323 1.356 0.175692
## category_code_LT01_15_count -0.18765 0.76441 -0.245 0.806185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6214
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.622118998536965
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9786 -0.7675 0.0232 0.8825 3.7329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93994 0.09097 109.269 < 2e-16 ***
## category_code_LT01_1_count 0.30076 0.08850 3.398 0.000733 ***
## category_code_LT01_4_count 0.88966 0.08049 11.053 < 2e-16 ***
## category_code_LT01_5_count 0.94322 0.06200 15.214 < 2e-16 ***
## category_code_LT01_9_count 0.45029 0.22604 1.992 0.046916 *
## category_code_LT01_10_count 0.14611 0.11277 1.296 0.195693
## category_code_LT01_16_count 1.13357 1.16463 0.973 0.330872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6221
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.629464950624145
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0119 -0.7694 0.0536 0.9009 3.6161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97944 0.08671 115.086 < 2e-16 ***
## category_code_LT01_1_count 0.24206 0.08947 2.705 0.007060 **
## category_code_LT01_4_count 0.73517 0.09304 7.901 1.83e-14 ***
## category_code_LT01_5_count 0.93428 0.06177 15.126 < 2e-16 ***
## category_code_LT01_9_count 0.42538 0.22269 1.910 0.056684 .
## category_code_LT01_11_count 0.40188 0.11480 3.501 0.000506 ***
## category_code_LT01_12_count -0.08074 0.21096 -0.383 0.702080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6339, Adjusted R-squared: 0.6295
## F-statistic: 141.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.629362265500935
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0098 -0.7687 0.0481 0.9147 3.6273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97909 0.08672 115.072 < 2e-16 ***
## category_code_LT01_1_count 0.23798 0.08999 2.644 0.008443 **
## category_code_LT01_4_count 0.73488 0.09320 7.885 2.06e-14 ***
## category_code_LT01_5_count 0.93179 0.06154 15.140 < 2e-16 ***
## category_code_LT01_9_count 0.42759 0.22325 1.915 0.056035 .
## category_code_LT01_11_count 0.39023 0.11111 3.512 0.000486 ***
## category_code_LT01_13_count 0.02489 0.24388 0.102 0.918750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6338, Adjusted R-squared: 0.6294
## F-statistic: 141.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.629356961781572
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0099 -0.7683 0.0471 0.9119 3.6261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97876 0.08685 114.898 < 2e-16 ***
## category_code_LT01_1_count 0.23971 0.08959 2.676 0.007706 **
## category_code_LT01_4_count 0.73606 0.09370 7.855 2.53e-14 ***
## category_code_LT01_5_count 0.93241 0.06183 15.080 < 2e-16 ***
## category_code_LT01_9_count 0.42682 0.22315 1.913 0.056369 .
## category_code_LT01_11_count 0.39070 0.11102 3.519 0.000473 ***
## category_code_LT01_14_count -0.01901 0.32642 -0.058 0.953592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6338, Adjusted R-squared: 0.6294
## F-statistic: 141.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.629402359375596
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0096 -0.7682 0.0597 0.9097 3.6232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97868 0.08673 115.060 < 2e-16 ***
## category_code_LT01_1_count 0.24308 0.09048 2.687 0.007463 **
## category_code_LT01_4_count 0.73622 0.09310 7.908 1.75e-14 ***
## category_code_LT01_5_count 0.93201 0.06149 15.156 < 2e-16 ***
## category_code_LT01_9_count 0.42436 0.22279 1.905 0.057399 .
## category_code_LT01_11_count 0.39171 0.11109 3.526 0.000461 ***
## category_code_LT01_15_count -0.18998 0.75369 -0.252 0.801095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6339, Adjusted R-squared: 0.6294
## F-statistic: 141.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.629967758191604
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0090 -0.7685 0.0505 0.9177 3.6278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97892 0.08665 115.167 < 2e-16 ***
## category_code_LT01_1_count 0.24109 0.08913 2.705 0.007068 **
## category_code_LT01_4_count 0.73303 0.09302 7.881 2.12e-14 ***
## category_code_LT01_5_count 0.93076 0.06146 15.144 < 2e-16 ***
## category_code_LT01_9_count 0.41717 0.22274 1.873 0.061683 .
## category_code_LT01_11_count 0.38666 0.11102 3.483 0.000541 ***
## category_code_LT01_16_count 1.03951 1.15226 0.902 0.367421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.620258819456645
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0093 -0.7772 0.0385 0.9208 3.8700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97194 0.08776 113.631 <2e-16 ***
## category_code_LT01_1_count 0.28848 0.09035 3.193 0.0015 **
## category_code_LT01_4_count 0.90245 0.08085 11.162 <2e-16 ***
## category_code_LT01_5_count 0.94137 0.06254 15.052 <2e-16 ***
## category_code_LT01_9_count 0.50070 0.22497 2.226 0.0265 *
## category_code_LT01_12_count 0.10683 0.20653 0.517 0.6052
## category_code_LT01_13_count 0.05788 0.24668 0.235 0.8146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6203
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.62022152636596
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0095 -0.7796 0.0326 0.9260 3.8680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97139 0.08789 113.448 < 2e-16 ***
## category_code_LT01_1_count 0.29214 0.08980 3.253 0.00122 **
## category_code_LT01_4_count 0.90500 0.08121 11.144 < 2e-16 ***
## category_code_LT01_5_count 0.94247 0.06279 15.010 < 2e-16 ***
## category_code_LT01_9_count 0.49837 0.22493 2.216 0.02717 *
## category_code_LT01_12_count 0.10852 0.20693 0.524 0.60023
## category_code_LT01_14_count -0.02737 0.33107 -0.083 0.93415
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.620224538362312
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0095 -0.7790 0.0377 0.9266 3.8670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97163 0.08777 113.608 < 2e-16 ***
## category_code_LT01_1_count 0.29319 0.09096 3.223 0.00135 **
## category_code_LT01_4_count 0.90471 0.08072 11.208 < 2e-16 ***
## category_code_LT01_5_count 0.94199 0.06249 15.074 < 2e-16 ***
## category_code_LT01_9_count 0.49661 0.22456 2.212 0.02746 *
## category_code_LT01_12_count 0.10667 0.20666 0.516 0.60599
## category_code_LT01_15_count -0.07902 0.76296 -0.104 0.91755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.621034205033613
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0084 -0.7726 0.0398 0.9210 3.8671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97174 0.08767 113.747 < 2e-16 ***
## category_code_LT01_1_count 0.29297 0.08936 3.278 0.00112 **
## category_code_LT01_4_count 0.89933 0.08058 11.161 < 2e-16 ***
## category_code_LT01_5_count 0.94035 0.06244 15.059 < 2e-16 ***
## category_code_LT01_9_count 0.48614 0.22450 2.165 0.03084 *
## category_code_LT01_12_count 0.10690 0.20630 0.518 0.60457
## category_code_LT01_16_count 1.19946 1.16514 1.029 0.30377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.620053616654535
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0120 -0.7869 0.0317 0.9179 3.8656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97197 0.08791 113.431 < 2e-16 ***
## category_code_LT01_1_count 0.29478 0.09009 3.272 0.00114 **
## category_code_LT01_4_count 0.90924 0.08076 11.259 < 2e-16 ***
## category_code_LT01_5_count 0.94514 0.06254 15.112 < 2e-16 ***
## category_code_LT01_9_count 0.50341 0.22543 2.233 0.02600 *
## category_code_LT01_13_count 0.05937 0.24674 0.241 0.80996
## category_code_LT01_14_count -0.01557 0.33052 -0.047 0.96244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.620059770288643
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0118 -0.7860 0.0358 0.9189 3.8646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97204 0.08779 113.585 < 2e-16 ***
## category_code_LT01_1_count 0.29612 0.09132 3.243 0.00126 **
## category_code_LT01_4_count 0.90928 0.08017 11.342 < 2e-16 ***
## category_code_LT01_5_count 0.94485 0.06220 15.191 < 2e-16 ***
## category_code_LT01_9_count 0.50201 0.22511 2.230 0.02619 *
## category_code_LT01_13_count 0.05723 0.24775 0.231 0.81742
## category_code_LT01_15_count -0.07724 0.76580 -0.101 0.91970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.620883653997471
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0108 -0.7746 0.0352 0.8974 3.8649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97216 0.08768 113.729 < 2e-16 ***
## category_code_LT01_1_count 0.29546 0.08956 3.299 0.00104 **
## category_code_LT01_4_count 0.90360 0.08002 11.292 < 2e-16 ***
## category_code_LT01_5_count 0.94311 0.06215 15.174 < 2e-16 ***
## category_code_LT01_9_count 0.49201 0.22499 2.187 0.02922 *
## category_code_LT01_13_count 0.06680 0.24655 0.271 0.78654
## category_code_LT01_16_count 1.21002 1.16585 1.038 0.29983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6209
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.620020455094672
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0121 -0.7908 0.0324 0.9152 3.8624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97162 0.08793 113.408 <2e-16 ***
## category_code_LT01_1_count 0.29989 0.09060 3.310 0.001 **
## category_code_LT01_4_count 0.91167 0.08058 11.314 <2e-16 ***
## category_code_LT01_5_count 0.94579 0.06249 15.136 <2e-16 ***
## category_code_LT01_9_count 0.49917 0.22504 2.218 0.027 *
## category_code_LT01_14_count -0.01669 0.33051 -0.050 0.960
## category_code_LT01_15_count -0.09352 0.76268 -0.123 0.902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.620826971397273
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0111 -0.7803 0.0341 0.9184 3.8627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9720048 0.0878201 113.550 < 2e-16 ***
## category_code_LT01_1_count 0.2989320 0.0890615 3.356 0.000851 ***
## category_code_LT01_4_count 0.9056100 0.0805015 11.250 < 2e-16 ***
## category_code_LT01_5_count 0.9438124 0.0624502 15.113 < 2e-16 ***
## category_code_LT01_9_count 0.4880531 0.2250215 2.169 0.030568 *
## category_code_LT01_14_count 0.0005553 0.3305738 0.002 0.998660
## category_code_LT01_16_count 1.2011056 1.1669235 1.029 0.303849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6208
## F-statistic: 136.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.620834398900729
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0109 -0.7811 0.0375 0.9175 3.8617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97185 0.08770 113.703 < 2e-16 ***
## category_code_LT01_1_count 0.30052 0.09008 3.336 0.000914 ***
## category_code_LT01_4_count 0.90614 0.07985 11.348 < 2e-16 ***
## category_code_LT01_5_count 0.94383 0.06210 15.198 < 2e-16 ***
## category_code_LT01_9_count 0.48753 0.22460 2.171 0.030436 *
## category_code_LT01_15_count -0.07475 0.76206 -0.098 0.921903
## category_code_LT01_16_count 1.19830 1.16577 1.028 0.304505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6208
## F-statistic: 136.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.628155325182491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9895 -0.7561 0.0489 0.8955 3.4617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95099 0.09030 110.199 < 2e-16 ***
## category_code_LT01_1_count 0.25221 0.08958 2.815 0.005068 **
## category_code_LT01_4_count 0.73967 0.09327 7.931 1.48e-14 ***
## category_code_LT01_5_count 0.94294 0.06167 15.290 < 2e-16 ***
## category_code_LT01_10_count 0.15350 0.11116 1.381 0.167938
## category_code_LT01_11_count 0.41227 0.11473 3.593 0.000359 ***
## category_code_LT01_12_count -0.09416 0.21146 -0.445 0.656309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6326, Adjusted R-squared: 0.6282
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.628009197146215
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7540 0.0370 0.9187 3.4740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95083 0.09033 110.160 < 2e-16 ***
## category_code_LT01_1_count 0.24975 0.09008 2.773 0.005773 **
## category_code_LT01_4_count 0.74048 0.09338 7.930 1.49e-14 ***
## category_code_LT01_5_count 0.94051 0.06144 15.307 < 2e-16 ***
## category_code_LT01_10_count 0.15197 0.11116 1.367 0.172206
## category_code_LT01_11_count 0.39963 0.11110 3.597 0.000354 ***
## category_code_LT01_13_count -0.01779 0.24385 -0.073 0.941861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6325, Adjusted R-squared: 0.628
## F-statistic: 140.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.628048585505905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7528 0.0365 0.9016 3.4695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94850 0.09089 109.462 < 2e-16 ***
## category_code_LT01_1_count 0.25114 0.08978 2.797 0.005357 **
## category_code_LT01_4_count 0.74246 0.09376 7.918 1.62e-14 ***
## category_code_LT01_5_count 0.94196 0.06177 15.249 < 2e-16 ***
## category_code_LT01_10_count 0.15763 0.11381 1.385 0.166665
## category_code_LT01_11_count 0.39916 0.11104 3.595 0.000357 ***
## category_code_LT01_14_count -0.08003 0.33429 -0.239 0.810883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6325, Adjusted R-squared: 0.628
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.628141631840443
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9860 -0.7523 0.0225 0.8995 3.4666
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94939 0.09038 110.090 < 2e-16 ***
## category_code_LT01_1_count 0.25544 0.09060 2.819 0.005006 **
## category_code_LT01_4_count 0.74103 0.09329 7.944 1.35e-14 ***
## category_code_LT01_5_count 0.94021 0.06140 15.313 < 2e-16 ***
## category_code_LT01_10_count 0.15573 0.11149 1.397 0.163107
## category_code_LT01_11_count 0.40068 0.11106 3.608 0.000341 ***
## category_code_LT01_15_count -0.32149 0.75736 -0.424 0.671394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6326, Adjusted R-squared: 0.6281
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.628645225564955
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9874 -0.7536 0.0538 0.8986 3.4811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95178 0.09025 110.275 < 2e-16 ***
## category_code_LT01_1_count 0.25056 0.08923 2.808 0.005184 **
## category_code_LT01_4_count 0.73778 0.09324 7.913 1.68e-14 ***
## category_code_LT01_5_count 0.93889 0.06138 15.297 < 2e-16 ***
## category_code_LT01_10_count 0.14687 0.11114 1.322 0.186941
## category_code_LT01_11_count 0.39519 0.11104 3.559 0.000408 ***
## category_code_LT01_16_count 1.06206 1.15450 0.920 0.358058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6331, Adjusted R-squared: 0.6286
## F-statistic: 141.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.618377770959382
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9836 -0.7744 0.0384 0.9130 3.6965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.938657 0.091428 108.705 < 2e-16 ***
## category_code_LT01_1_count 0.304426 0.090397 3.368 0.000818 ***
## category_code_LT01_4_count 0.914588 0.080769 11.323 < 2e-16 ***
## category_code_LT01_5_count 0.952304 0.062458 15.247 < 2e-16 ***
## category_code_LT01_10_count 0.178120 0.112454 1.584 0.113853
## category_code_LT01_12_count 0.097518 0.207301 0.470 0.638264
## category_code_LT01_13_count 0.008836 0.246875 0.036 0.971462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.618442321356576
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9819 -0.7754 0.0328 0.9150 3.6895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93562 0.09198 108.020 < 2e-16 ***
## category_code_LT01_1_count 0.30744 0.08996 3.418 0.000684 ***
## category_code_LT01_4_count 0.91731 0.08102 11.322 < 2e-16 ***
## category_code_LT01_5_count 0.95424 0.06274 15.209 < 2e-16 ***
## category_code_LT01_10_count 0.18540 0.11505 1.611 0.107718
## category_code_LT01_12_count 0.10081 0.20757 0.486 0.627422
## category_code_LT01_14_count -0.09847 0.33905 -0.290 0.771614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.618447482547659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9825 -0.7645 0.0372 0.9106 3.6905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93744 0.09148 108.624 < 2e-16 ***
## category_code_LT01_1_count 0.30989 0.09106 3.403 0.00072 ***
## category_code_LT01_4_count 0.91598 0.08065 11.357 < 2e-16 ***
## category_code_LT01_5_count 0.95239 0.06242 15.259 < 2e-16 ***
## category_code_LT01_10_count 0.18128 0.11283 1.607 0.10878
## category_code_LT01_12_count 0.09499 0.20745 0.458 0.64724
## category_code_LT01_15_count -0.23152 0.76754 -0.302 0.76305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6231, Adjusted R-squared: 0.6184
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.619236629535347
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9834 -0.7712 0.0331 0.9189 3.7007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93973 0.09132 108.849 < 2e-16 ***
## category_code_LT01_1_count 0.30608 0.08944 3.422 0.000673 ***
## category_code_LT01_4_count 0.90985 0.08061 11.287 < 2e-16 ***
## category_code_LT01_5_count 0.95050 0.06238 15.238 < 2e-16 ***
## category_code_LT01_10_count 0.17228 0.11241 1.533 0.126018
## category_code_LT01_12_count 0.09745 0.20706 0.471 0.638117
## category_code_LT01_16_count 1.22995 1.16805 1.053 0.292862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.61825993665187
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9840 -0.7763 0.0292 0.8984 3.6837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.935568 0.092020 107.972 < 2e-16 ***
## category_code_LT01_1_count 0.312446 0.090192 3.464 0.000578 ***
## category_code_LT01_4_count 0.922557 0.080489 11.462 < 2e-16 ***
## category_code_LT01_5_count 0.957273 0.062474 15.323 < 2e-16 ***
## category_code_LT01_10_count 0.187521 0.115063 1.630 0.103802
## category_code_LT01_13_count 0.008453 0.246969 0.034 0.972709
## category_code_LT01_14_count -0.089409 0.338743 -0.264 0.791934
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.6183
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.6182846478267
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9842 -0.7688 0.0320 0.9027 3.6839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93703 0.09152 108.576 < 2e-16 ***
## category_code_LT01_1_count 0.31541 0.09133 3.454 0.000601 ***
## category_code_LT01_4_count 0.92135 0.08003 11.512 < 2e-16 ***
## category_code_LT01_5_count 0.95547 0.06210 15.386 < 2e-16 ***
## category_code_LT01_10_count 0.18416 0.11277 1.633 0.103097
## category_code_LT01_13_count 0.00269 0.24795 0.011 0.991347
## category_code_LT01_15_count -0.24537 0.77033 -0.319 0.750223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.6183
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.619069303018715
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9853 -0.7603 0.0346 0.9071 3.6950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93948 0.09135 108.809 < 2e-16 ***
## category_code_LT01_1_count 0.31064 0.08957 3.468 0.000571 ***
## category_code_LT01_4_count 0.91490 0.08000 11.436 < 2e-16 ***
## category_code_LT01_5_count 0.95351 0.06206 15.363 < 2e-16 ***
## category_code_LT01_10_count 0.17480 0.11234 1.556 0.120368
## category_code_LT01_13_count 0.01866 0.24678 0.076 0.939764
## category_code_LT01_16_count 1.23326 1.16896 1.055 0.291940
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.618342620572481
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9826 -0.7725 0.0245 0.8964 3.6772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93417 0.09208 107.886 < 2e-16 ***
## category_code_LT01_1_count 0.31826 0.09076 3.507 0.000495 ***
## category_code_LT01_4_count 0.92398 0.08033 11.502 < 2e-16 ***
## category_code_LT01_5_count 0.95733 0.06242 15.336 < 2e-16 ***
## category_code_LT01_10_count 0.19109 0.11544 1.655 0.098508 .
## category_code_LT01_14_count -0.09258 0.33873 -0.273 0.784728
## category_code_LT01_15_count -0.25161 0.76724 -0.328 0.743097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6183
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.619096832078491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9841 -0.7637 0.0329 0.9112 3.6893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93725 0.09192 108.113 < 2e-16 ***
## category_code_LT01_1_count 0.31349 0.08918 3.515 0.00048 ***
## category_code_LT01_4_count 0.91730 0.08034 11.418 < 2e-16 ***
## category_code_LT01_5_count 0.95508 0.06240 15.306 < 2e-16 ***
## category_code_LT01_10_count 0.18022 0.11508 1.566 0.11797
## category_code_LT01_14_count -0.06879 0.33888 -0.203 0.83923
## category_code_LT01_16_count 1.21622 1.17033 1.039 0.29922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.619130262466754
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9841 -0.7612 0.0355 0.9083 3.6889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93825 0.09141 108.727 < 2e-16 ***
## category_code_LT01_1_count 0.31626 0.09013 3.509 0.000491 ***
## category_code_LT01_4_count 0.91640 0.07985 11.477 < 2e-16 ***
## category_code_LT01_5_count 0.95363 0.06202 15.377 < 2e-16 ***
## category_code_LT01_10_count 0.17798 0.11271 1.579 0.114943
## category_code_LT01_15_count -0.22256 0.76652 -0.290 0.771671
## category_code_LT01_16_count 1.22032 1.16872 1.044 0.296930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.626711875270196
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0245 -0.7704 0.0252 0.8917 3.5911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.985297 0.086982 114.797 < 2e-16 ***
## category_code_LT01_1_count 0.249415 0.090505 2.756 0.006072 **
## category_code_LT01_4_count 0.754392 0.092961 8.115 3.93e-15 ***
## category_code_LT01_5_count 0.943982 0.061822 15.269 < 2e-16 ***
## category_code_LT01_11_count 0.422196 0.114792 3.678 0.000261 ***
## category_code_LT01_12_count -0.083674 0.211737 -0.395 0.692880
## category_code_LT01_13_count -0.007256 0.244161 -0.030 0.976306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6267
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.62671700138879
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0245 -0.7688 0.0289 0.8943 3.5914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98572 0.08710 114.644 < 2e-16 ***
## category_code_LT01_1_count 0.24833 0.09012 2.756 0.00607 **
## category_code_LT01_4_count 0.75323 0.09358 8.049 6.35e-15 ***
## category_code_LT01_5_count 0.94336 0.06212 15.187 < 2e-16 ***
## category_code_LT01_11_count 0.42217 0.11474 3.679 0.00026 ***
## category_code_LT01_12_count -0.08487 0.21216 -0.400 0.68931
## category_code_LT01_14_count 0.02861 0.32760 0.087 0.93045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6267
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.626791900214761
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0240 -0.7712 0.0210 0.8889 3.5872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98483 0.08698 114.791 < 2e-16 ***
## category_code_LT01_1_count 0.25416 0.09107 2.791 0.005463 **
## category_code_LT01_4_count 0.75519 0.09288 8.131 3.51e-15 ***
## category_code_LT01_5_count 0.94392 0.06178 15.278 < 2e-16 ***
## category_code_LT01_11_count 0.42376 0.11484 3.690 0.000249 ***
## category_code_LT01_12_count -0.08693 0.21195 -0.410 0.681857
## category_code_LT01_15_count -0.24660 0.75685 -0.326 0.744691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.627438311839759
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0231 -0.7647 0.0381 0.8865 3.5939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98505 0.08690 114.909 < 2e-16 ***
## category_code_LT01_1_count 0.25088 0.08966 2.798 0.005342 **
## category_code_LT01_4_count 0.75123 0.09281 8.094 4.57e-15 ***
## category_code_LT01_5_count 0.94227 0.06175 15.259 < 2e-16 ***
## category_code_LT01_11_count 0.41702 0.11474 3.635 0.000308 ***
## category_code_LT01_12_count -0.08198 0.21154 -0.388 0.698527
## category_code_LT01_16_count 1.13074 1.15511 0.979 0.328108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.626596023178806
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7646 0.0255 0.9063 3.6016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.985176 0.087110 114.627 < 2e-16 ***
## category_code_LT01_1_count 0.245942 0.090665 2.713 0.006909 **
## category_code_LT01_4_count 0.753967 0.093707 8.046 6.48e-15 ***
## category_code_LT01_5_count 0.941259 0.061929 15.199 < 2e-16 ***
## category_code_LT01_11_count 0.410569 0.111019 3.698 0.000242 ***
## category_code_LT01_13_count -0.007306 0.244229 -0.030 0.976149
## category_code_LT01_14_count 0.020114 0.327031 0.062 0.950982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6266
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.626666651197985
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0220 -0.7639 0.0255 0.8984 3.5976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98440 0.08700 114.768 < 2e-16 ***
## category_code_LT01_1_count 0.25159 0.09169 2.744 0.006291 **
## category_code_LT01_4_count 0.75573 0.09302 8.124 3.68e-15 ***
## category_code_LT01_5_count 0.94165 0.06155 15.299 < 2e-16 ***
## category_code_LT01_11_count 0.41191 0.11108 3.708 0.000233 ***
## category_code_LT01_13_count -0.01442 0.24518 -0.059 0.953110
## category_code_LT01_15_count -0.23606 0.75924 -0.311 0.755992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6267
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.62732435665266
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0211 -0.7638 0.0335 0.8948 3.6041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9846570 0.0869054 114.891 < 2e-16 ***
## category_code_LT01_1_count 0.2479977 0.0901419 2.751 0.006157 **
## category_code_LT01_4_count 0.7514965 0.0929386 8.086 4.85e-15 ***
## category_code_LT01_5_count 0.9400009 0.0615192 15.280 < 2e-16 ***
## category_code_LT01_11_count 0.4056572 0.1110220 3.654 0.000286 ***
## category_code_LT01_13_count 0.0004019 0.2440939 0.002 0.998687
## category_code_LT01_16_count 1.1345150 1.1558829 0.982 0.326823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.626666746920205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0219 -0.7641 0.0262 0.9012 3.5984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98472 0.08711 114.619 < 2e-16 ***
## category_code_LT01_1_count 0.25028 0.09116 2.746 0.006262 **
## category_code_LT01_4_count 0.75474 0.09363 8.061 5.8e-15 ***
## category_code_LT01_5_count 0.94112 0.06188 15.208 < 2e-16 ***
## category_code_LT01_11_count 0.41161 0.11101 3.708 0.000233 ***
## category_code_LT01_14_count 0.01958 0.32697 0.060 0.952266
## category_code_LT01_15_count -0.23172 0.75616 -0.306 0.759395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6312, Adjusted R-squared: 0.6267
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.627333310696395
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0211 -0.7642 0.0352 0.8980 3.6044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98515 0.08702 114.745 < 2e-16 ***
## category_code_LT01_1_count 0.24707 0.08979 2.752 0.006150 **
## category_code_LT01_4_count 0.75022 0.09357 8.018 7.92e-15 ***
## category_code_LT01_5_count 0.93926 0.06186 15.184 < 2e-16 ***
## category_code_LT01_11_count 0.40554 0.11096 3.655 0.000285 ***
## category_code_LT01_14_count 0.03552 0.32703 0.109 0.913542
## category_code_LT01_16_count 1.14040 1.15653 0.986 0.324594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.627384389638461
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0206 -0.7634 0.0302 0.8908 3.6008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98423 0.08691 114.882 < 2e-16 ***
## category_code_LT01_1_count 0.25229 0.09065 2.783 0.005589 **
## category_code_LT01_4_count 0.75235 0.09286 8.102 4.33e-15 ***
## category_code_LT01_5_count 0.93994 0.06148 15.289 < 2e-16 ***
## category_code_LT01_11_count 0.40675 0.11101 3.664 0.000275 ***
## category_code_LT01_15_count -0.21254 0.75568 -0.281 0.778627
## category_code_LT01_16_count 1.12585 1.15556 0.974 0.330392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616430351627558
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0242 -0.7682 0.0044 0.9342 3.8546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97861 0.08828 113.038 < 2e-16 ***
## category_code_LT01_1_count 0.30109 0.09101 3.308 0.00101 **
## category_code_LT01_4_count 0.93545 0.08065 11.599 < 2e-16 ***
## category_code_LT01_5_count 0.95322 0.06295 15.143 < 2e-16 ***
## category_code_LT01_12_count 0.11415 0.20796 0.549 0.58332
## category_code_LT01_13_count 0.02180 0.24741 0.088 0.92981
## category_code_LT01_14_count 0.01893 0.33212 0.057 0.95458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616445833444088
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0240 -0.7676 0.0027 0.9340 3.8529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97809 0.08817 113.175 < 2e-16 ***
## category_code_LT01_1_count 0.30426 0.09231 3.296 0.00105 **
## category_code_LT01_4_count 0.93698 0.08003 11.709 < 2e-16 ***
## category_code_LT01_5_count 0.95364 0.06261 15.231 < 2e-16 ***
## category_code_LT01_12_count 0.11378 0.20767 0.548 0.58402
## category_code_LT01_13_count 0.01823 0.24835 0.073 0.94152
## category_code_LT01_15_count -0.11687 0.76953 -0.152 0.87935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617426925398362
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7677 0.0107 0.9407 3.8540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97816 0.08804 113.341 < 2e-16 ***
## category_code_LT01_1_count 0.30251 0.09050 3.343 0.000893 ***
## category_code_LT01_4_count 0.92978 0.07991 11.635 < 2e-16 ***
## category_code_LT01_5_count 0.95144 0.06256 15.209 < 2e-16 ***
## category_code_LT01_12_count 0.11403 0.20727 0.550 0.582452
## category_code_LT01_13_count 0.03043 0.24718 0.123 0.902071
## category_code_LT01_16_count 1.32479 1.16992 1.132 0.258029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616443977106873
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0240 -0.7678 0.0027 0.9351 3.8523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97828 0.08828 113.024 < 2e-16 ***
## category_code_LT01_1_count 0.30482 0.09164 3.326 0.000946 ***
## category_code_LT01_4_count 0.93685 0.08056 11.629 < 2e-16 ***
## category_code_LT01_5_count 0.95345 0.06291 15.157 < 2e-16 ***
## category_code_LT01_12_count 0.11318 0.20808 0.544 0.586736
## category_code_LT01_14_count 0.01822 0.33207 0.055 0.956260
## category_code_LT01_15_count -0.12170 0.76651 -0.159 0.873913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.61742438126997
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0226 -0.7681 0.0108 0.9415 3.8532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97857 0.08816 113.190 < 2e-16 ***
## category_code_LT01_1_count 0.30314 0.09002 3.367 0.000819 ***
## category_code_LT01_4_count 0.92929 0.08051 11.543 < 2e-16 ***
## category_code_LT01_5_count 0.95100 0.06286 15.129 < 2e-16 ***
## category_code_LT01_12_count 0.11289 0.20768 0.544 0.586989
## category_code_LT01_14_count 0.03620 0.33201 0.109 0.913213
## category_code_LT01_16_count 1.32626 1.17064 1.133 0.257797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617428535099069
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0224 -0.7674 0.0028 0.9399 3.8518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97785 0.08805 113.324 < 2e-16 ***
## category_code_LT01_1_count 0.30620 0.09114 3.360 0.000841 ***
## category_code_LT01_4_count 0.93126 0.07983 11.665 < 2e-16 ***
## category_code_LT01_5_count 0.95174 0.06252 15.223 < 2e-16 ***
## category_code_LT01_12_count 0.11333 0.20739 0.546 0.584994
## category_code_LT01_15_count -0.10049 0.76576 -0.131 0.895646
## category_code_LT01_16_count 1.31642 1.16969 1.125 0.260951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616217788811146
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0268 -0.7683 -0.0039 0.9192 3.8482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97879 0.08831 112.993 < 2e-16 ***
## category_code_LT01_1_count 0.31007 0.09202 3.370 0.000812 ***
## category_code_LT01_4_count 0.94278 0.08001 11.784 < 2e-16 ***
## category_code_LT01_5_count 0.95675 0.06264 15.274 < 2e-16 ***
## category_code_LT01_13_count 0.01973 0.24844 0.079 0.936732
## category_code_LT01_14_count 0.03010 0.33154 0.091 0.927698
## category_code_LT01_15_count -0.13143 0.76929 -0.171 0.864413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6162
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617207748246463
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.7687 0.0042 0.9212 3.8496
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97914 0.08818 113.164 < 2e-16 ***
## category_code_LT01_1_count 0.30750 0.09026 3.407 0.000711 ***
## category_code_LT01_4_count 0.93475 0.07997 11.689 < 2e-16 ***
## category_code_LT01_5_count 0.95416 0.06260 15.242 < 2e-16 ***
## category_code_LT01_13_count 0.03264 0.24727 0.132 0.895041
## category_code_LT01_14_count 0.04847 0.33148 0.146 0.883805
## category_code_LT01_16_count 1.33531 1.17157 1.140 0.254940
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617206415864274
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0252 -0.7678 0.0022 0.9203 3.8479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97823 0.08808 113.290 < 2e-16 ***
## category_code_LT01_1_count 0.31126 0.09142 3.405 0.000716 ***
## category_code_LT01_4_count 0.93735 0.07916 11.842 < 2e-16 ***
## category_code_LT01_5_count 0.95520 0.06221 15.355 < 2e-16 ***
## category_code_LT01_13_count 0.02889 0.24823 0.116 0.907397
## category_code_LT01_15_count -0.10780 0.76857 -0.140 0.888514
## category_code_LT01_16_count 1.32261 1.17070 1.130 0.259130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617211756463412
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0253 -0.7683 0.0027 0.9338 3.8471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97878 0.08819 113.147 < 2e-16 ***
## category_code_LT01_1_count 0.31159 0.09079 3.432 0.00065 ***
## category_code_LT01_4_count 0.93638 0.07983 11.730 < 2e-16 ***
## category_code_LT01_5_count 0.95448 0.06255 15.259 < 2e-16 ***
## category_code_LT01_14_count 0.04733 0.33144 0.143 0.88650
## category_code_LT01_15_count -0.11499 0.76548 -0.150 0.88066
## category_code_LT01_16_count 1.32586 1.17132 1.132 0.25821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.601239781506689
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0877 -0.8179 0.0204 0.9578 3.4215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01287 0.08991 111.371 < 2e-16 ***
## category_code_LT01_1_count 0.42272 0.08765 4.823 1.89e-06 ***
## category_code_LT01_5_count 0.99533 0.06381 15.597 < 2e-16 ***
## category_code_LT01_6_count 0.63652 0.15273 4.168 3.64e-05 ***
## category_code_LT01_7_count 0.57735 0.16070 3.593 0.00036 ***
## category_code_LT01_8_count -0.18179 0.28217 -0.644 0.51971
## category_code_LT01_11_count 0.61634 0.10944 5.632 3.01e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 491 degrees of freedom
## Multiple R-squared: 0.6061, Adjusted R-squared: 0.6012
## F-statistic: 125.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.604194095724545
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0678 -0.8030 0.0383 0.9196 3.4296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00474 0.08961 111.652 < 2e-16 ***
## category_code_LT01_1_count 0.40998 0.08743 4.689 3.56e-06 ***
## category_code_LT01_5_count 0.97839 0.06309 15.509 < 2e-16 ***
## category_code_LT01_6_count 0.60888 0.15244 3.994 7.49e-05 ***
## category_code_LT01_7_count 0.53999 0.16083 3.358 0.000847 ***
## category_code_LT01_9_count 0.46614 0.23069 2.021 0.043859 *
## category_code_LT01_11_count 0.59764 0.10946 5.460 7.58e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.609, Adjusted R-squared: 0.6042
## F-statistic: 127.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.601957939889953
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0528 -0.7885 0.0226 0.9788 3.4520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98240 0.09334 106.950 < 2e-16 ***
## category_code_LT01_1_count 0.42255 0.08753 4.827 1.85e-06 ***
## category_code_LT01_5_count 0.98900 0.06304 15.689 < 2e-16 ***
## category_code_LT01_6_count 0.59995 0.15502 3.870 0.000123 ***
## category_code_LT01_7_count 0.55639 0.16111 3.453 0.000601 ***
## category_code_LT01_10_count 0.13306 0.11663 1.141 0.254461
## category_code_LT01_11_count 0.61298 0.10938 5.604 3.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.421 on 491 degrees of freedom
## Multiple R-squared: 0.6068, Adjusted R-squared: 0.602
## F-statistic: 126.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.601135284894325
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0843 -0.8013 0.0299 0.9621 3.4227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01170 0.08989 111.376 < 2e-16 ***
## category_code_LT01_1_count 0.42456 0.08790 4.830 1.83e-06 ***
## category_code_LT01_5_count 0.99215 0.06335 15.662 < 2e-16 ***
## category_code_LT01_6_count 0.63822 0.15302 4.171 3.59e-05 ***
## category_code_LT01_7_count 0.56791 0.16091 3.529 0.000456 ***
## category_code_LT01_11_count 0.63545 0.11333 5.607 3.44e-08 ***
## category_code_LT01_12_count -0.11766 0.21989 -0.535 0.592828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.422 on 491 degrees of freedom
## Multiple R-squared: 0.606, Adjusted R-squared: 0.6011
## F-statistic: 125.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.600903597048547
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0819 -0.8085 0.0218 0.9662 3.4229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01146 0.08992 111.341 < 2e-16 ***
## category_code_LT01_1_count 0.42031 0.08860 4.744 2.75e-06 ***
## category_code_LT01_5_count 0.98910 0.06317 15.659 < 2e-16 ***
## category_code_LT01_6_count 0.63218 0.15266 4.141 4.07e-05 ***
## category_code_LT01_7_count 0.57267 0.16162 3.543 0.000433 ***
## category_code_LT01_11_count 0.61939 0.10942 5.660 2.57e-08 ***
## category_code_LT01_13_count 0.00848 0.25372 0.033 0.973350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6057, Adjusted R-squared: 0.6009
## F-statistic: 125.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.601764374203578
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0789 -0.8101 0.0382 0.9517 3.4196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01479 0.08988 111.427 < 2e-16 ***
## category_code_LT01_1_count 0.40702 0.08855 4.597 5.46e-06 ***
## category_code_LT01_5_count 0.97996 0.06368 15.388 < 2e-16 ***
## category_code_LT01_6_count 0.64291 0.15284 4.207 3.08e-05 ***
## category_code_LT01_7_count 0.55948 0.16103 3.474 0.000557 ***
## category_code_LT01_11_count 0.61139 0.10954 5.581 3.95e-08 ***
## category_code_LT01_14_count 0.34735 0.33700 1.031 0.303175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.421 on 491 degrees of freedom
## Multiple R-squared: 0.6066, Adjusted R-squared: 0.6018
## F-statistic: 126.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.600904953381398
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0819 -0.8112 0.0202 0.9662 3.4230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01136 0.08993 111.324 < 2e-16 ***
## category_code_LT01_1_count 0.42163 0.08924 4.725 3.01e-06 ***
## category_code_LT01_5_count 0.98918 0.06312 15.671 < 2e-16 ***
## category_code_LT01_6_count 0.63242 0.15277 4.140 4.09e-05 ***
## category_code_LT01_7_count 0.57287 0.16082 3.562 0.000404 ***
## category_code_LT01_11_count 0.61983 0.10956 5.658 2.61e-08 ***
## category_code_LT01_15_count -0.04132 0.78291 -0.053 0.957928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6057, Adjusted R-squared: 0.6009
## F-statistic: 125.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_1_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.602633756668945
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0777 -0.8035 0.0270 0.9699 3.4244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00995 0.08973 111.560 < 2e-16 ***
## category_code_LT01_1_count 0.42088 0.08744 4.813 1.98e-06 ***
## category_code_LT01_5_count 0.98513 0.06305 15.626 < 2e-16 ***
## category_code_LT01_6_count 0.64534 0.15258 4.230 2.79e-05 ***
## category_code_LT01_7_count 0.57314 0.16030 3.576 0.000384 ***
## category_code_LT01_11_count 0.60597 0.10953 5.532 5.14e-08 ***
## category_code_LT01_16_count 1.74675 1.19434 1.463 0.144238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.42 on 491 degrees of freedom
## Multiple R-squared: 0.6074, Adjusted R-squared: 0.6026
## F-statistic: 126.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.703503546831322
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7348 -0.6642 0.0180 0.6948 3.9611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.41698 0.08455 111.38 <2e-16 ***
## new_category_count_col 1.34594 0.03918 34.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.226 on 496 degrees of freedom
## Multiple R-squared: 0.7041, Adjusted R-squared: 0.7035
## F-statistic: 1180 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count 0.647020493344259
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9472 -0.7339 0.0096 0.8534 3.5109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94461 0.08465 117.478 < 2e-16 ***
## category_code_LT01_2_count 0.43371 0.09153 4.738 2.83e-06 ***
## category_code_LT01_3_count 0.21846 0.11174 1.955 0.05114 .
## category_code_LT01_4_count 0.52210 0.09970 5.237 2.43e-07 ***
## category_code_LT01_5_count 0.89113 0.06023 14.794 < 2e-16 ***
## category_code_LT01_6_count 0.29412 0.14923 1.971 0.04930 *
## category_code_LT01_7_count 0.39758 0.14982 2.654 0.00822 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.338 on 491 degrees of freedom
## Multiple R-squared: 0.6513, Adjusted R-squared: 0.647
## F-statistic: 152.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count 0.642202763685869
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9560 -0.7336 0.0862 0.8613 3.4966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94407 0.08526 116.634 < 2e-16 ***
## category_code_LT01_2_count 0.46521 0.09134 5.093 5.03e-07 ***
## category_code_LT01_3_count 0.22704 0.11249 2.018 0.0441 *
## category_code_LT01_4_count 0.58351 0.09768 5.974 4.46e-09 ***
## category_code_LT01_5_count 0.90466 0.06120 14.781 < 2e-16 ***
## category_code_LT01_6_count 0.29119 0.15035 1.937 0.0533 .
## category_code_LT01_8_count -0.15469 0.26691 -0.580 0.5625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6465, Adjusted R-squared: 0.6422
## F-statistic: 149.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.700443434479773
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7341 -0.6862 0.0200 0.6444 3.9672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.41092 0.08523 110.4 <2e-16 ***
## new_category_count_col 1.35368 0.03969 34.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.233 on 496 degrees of freedom
## Multiple R-squared: 0.701, Adjusted R-squared: 0.7004
## F-statistic: 1163 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count 0.643175370663541
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9455 -0.7290 0.0872 0.8758 3.5112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94055 0.08512 116.779 < 2e-16 ***
## category_code_LT01_2_count 0.45451 0.09165 4.959 9.77e-07 ***
## category_code_LT01_3_count 0.20676 0.11326 1.826 0.0685 .
## category_code_LT01_4_count 0.57931 0.09759 5.936 5.52e-09 ***
## category_code_LT01_5_count 0.89459 0.06060 14.763 < 2e-16 ***
## category_code_LT01_6_count 0.28131 0.15010 1.874 0.0615 .
## category_code_LT01_9_count 0.28657 0.22142 1.294 0.1962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 491 degrees of freedom
## Multiple R-squared: 0.6475, Adjusted R-squared: 0.6432
## F-statistic: 150.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count 0.642046751164874
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9433 -0.7360 0.0499 0.8694 3.4684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93472 0.08821 112.621 < 2e-16 ***
## category_code_LT01_2_count 0.46507 0.09140 5.088 5.16e-07 ***
## category_code_LT01_3_count 0.21938 0.11394 1.925 0.0548 .
## category_code_LT01_4_count 0.58356 0.09771 5.972 4.49e-09 ***
## category_code_LT01_5_count 0.89980 0.06058 14.853 < 2e-16 ***
## category_code_LT01_6_count 0.28027 0.15176 1.847 0.0654 .
## category_code_LT01_10_count 0.03907 0.11198 0.349 0.7273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6464, Adjusted R-squared: 0.642
## F-statistic: 149.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.704587535451595
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7391 -0.6657 -0.0072 0.6741 3.9331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.44501 0.08374 112.79 <2e-16 ***
## new_category_count_col 1.31177 0.03808 34.44 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.224 on 496 degrees of freedom
## Multiple R-squared: 0.7052, Adjusted R-squared: 0.7046
## F-statistic: 1186 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count 0.643618023132782
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9574 -0.7393 0.0250 0.8671 3.4977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94975 0.08518 116.802 < 2e-16 ***
## category_code_LT01_2_count 0.41790 0.09658 4.327 1.83e-05 ***
## category_code_LT01_3_count 0.19353 0.11424 1.694 0.0909 .
## category_code_LT01_4_count 0.54230 0.10114 5.362 1.27e-07 ***
## category_code_LT01_5_count 0.89845 0.06045 14.864 < 2e-16 ***
## category_code_LT01_6_count 0.26530 0.15066 1.761 0.0789 .
## category_code_LT01_11_count 0.17926 0.11854 1.512 0.1311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 491 degrees of freedom
## Multiple R-squared: 0.6479, Adjusted R-squared: 0.6436
## F-statistic: 150.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count 0.641986560131774
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9517 -0.7386 0.0423 0.8622 3.5026
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94262 0.08525 116.630 < 2e-16 ***
## category_code_LT01_2_count 0.46819 0.09191 5.094 5.01e-07 ***
## category_code_LT01_3_count 0.22672 0.11262 2.013 0.0446 *
## category_code_LT01_4_count 0.58401 0.09782 5.970 4.55e-09 ***
## category_code_LT01_5_count 0.90047 0.06077 14.818 < 2e-16 ***
## category_code_LT01_6_count 0.29055 0.15096 1.925 0.0548 .
## category_code_LT01_12_count -0.04001 0.20222 -0.198 0.8432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6463, Adjusted R-squared: 0.642
## F-statistic: 149.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count 0.642029727292823
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9506 -0.7343 0.0465 0.8683 3.5043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94259 0.08524 116.636 < 2e-16 ***
## category_code_LT01_2_count 0.46478 0.09146 5.082 5.32e-07 ***
## category_code_LT01_3_count 0.22532 0.11250 2.003 0.0457 *
## category_code_LT01_4_count 0.58022 0.09813 5.913 6.30e-09 ***
## category_code_LT01_5_count 0.89904 0.06060 14.837 < 2e-16 ***
## category_code_LT01_6_count 0.28873 0.15030 1.921 0.0553 .
## category_code_LT01_13_count 0.07431 0.23695 0.314 0.7539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6464, Adjusted R-squared: 0.642
## F-statistic: 149.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count 0.642092290185294
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9504 -0.7336 0.0543 0.8713 3.5055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94409 0.08530 116.574 < 2e-16 ***
## category_code_LT01_2_count 0.46286 0.09167 5.049 6.26e-07 ***
## category_code_LT01_3_count 0.22796 0.11261 2.024 0.0435 *
## category_code_LT01_4_count 0.57608 0.09905 5.816 1.09e-08 ***
## category_code_LT01_5_count 0.89654 0.06097 14.705 < 2e-16 ***
## category_code_LT01_6_count 0.29414 0.15100 1.948 0.0520 .
## category_code_LT01_14_count 0.13776 0.32097 0.429 0.6680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6464, Adjusted R-squared: 0.6421
## F-statistic: 149.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count 0.642017495875779
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9504 -0.7425 0.0688 0.8619 3.5045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94235 0.08525 116.624 < 2e-16 ***
## category_code_LT01_2_count 0.46695 0.09139 5.109 4.64e-07 ***
## category_code_LT01_3_count 0.22949 0.11328 2.026 0.0433 *
## category_code_LT01_4_count 0.58402 0.09776 5.974 4.45e-09 ***
## category_code_LT01_5_count 0.89901 0.06060 14.834 < 2e-16 ***
## category_code_LT01_6_count 0.28910 0.15035 1.923 0.0551 .
## category_code_LT01_15_count -0.21009 0.73555 -0.286 0.7753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6463, Adjusted R-squared: 0.642
## F-statistic: 149.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_16_count 0.642045657052995
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9510 -0.7362 0.0480 0.8653 3.5039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94296 0.08525 116.637 < 2e-16 ***
## category_code_LT01_2_count 0.46258 0.09194 5.032 6.84e-07 ***
## category_code_LT01_3_count 0.22145 0.11315 1.957 0.0509 .
## category_code_LT01_4_count 0.58478 0.09782 5.978 4.35e-09 ***
## category_code_LT01_5_count 0.89898 0.06059 14.836 < 2e-16 ***
## category_code_LT01_6_count 0.29294 0.15102 1.940 0.0530 .
## category_code_LT01_16_count 0.39899 1.15076 0.347 0.7290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6464, Adjusted R-squared: 0.642
## F-statistic: 149.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count 0.644479647915153
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9690 -0.7048 0.0089 0.8403 3.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95440 0.08489 117.259 < 2e-16 ***
## category_code_LT01_2_count 0.47215 0.08968 5.265 2.10e-07 ***
## category_code_LT01_3_count 0.23480 0.11191 2.098 0.03641 *
## category_code_LT01_4_count 0.55627 0.09857 5.643 2.83e-08 ***
## category_code_LT01_5_count 0.90843 0.06081 14.940 < 2e-16 ***
## category_code_LT01_7_count 0.39568 0.15042 2.631 0.00879 **
## category_code_LT01_8_count -0.15682 0.26599 -0.590 0.55575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6488, Adjusted R-squared: 0.6445
## F-statistic: 151.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.702132763984413
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7582 -0.6529 0.0121 0.6414 3.9421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.43597 0.08437 111.84 <2e-16 ***
## new_category_count_col 1.35236 0.03949 34.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.229 on 496 degrees of freedom
## Multiple R-squared: 0.7027, Adjusted R-squared: 0.7021
## F-statistic: 1173 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count 0.645151570179928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9587 -0.7017 0.0242 0.8453 3.4962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95078 0.08479 117.357 < 2e-16 ***
## category_code_LT01_2_count 0.46318 0.09000 5.147 3.84e-07 ***
## category_code_LT01_3_count 0.21675 0.11274 1.923 0.0551 .
## category_code_LT01_4_count 0.55426 0.09849 5.628 3.07e-08 ***
## category_code_LT01_5_count 0.89892 0.06020 14.932 < 2e-16 ***
## category_code_LT01_7_count 0.37750 0.15081 2.503 0.0126 *
## category_code_LT01_9_count 0.25048 0.22157 1.130 0.2588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6494, Adjusted R-squared: 0.6452
## F-statistic: 151.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count 0.644363825000801
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9538 -0.6923 0.0084 0.8468 3.4467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94284 0.08796 113.043 < 2e-16 ***
## category_code_LT01_2_count 0.47069 0.08983 5.240 2.39e-07 ***
## category_code_LT01_3_count 0.22517 0.11347 1.984 0.0478 *
## category_code_LT01_4_count 0.55613 0.09859 5.641 2.86e-08 ***
## category_code_LT01_5_count 0.90320 0.06015 15.016 < 2e-16 ***
## category_code_LT01_7_count 0.38819 0.15074 2.575 0.0103 *
## category_code_LT01_10_count 0.04798 0.11079 0.433 0.6651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6487, Adjusted R-squared: 0.6444
## F-statistic: 151.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.70489492000893
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7649 -0.6474 0.0118 0.6555 3.9054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.47268 0.08308 114.02 <2e-16 ***
## new_category_count_col 1.30903 0.03798 34.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.223 on 496 degrees of freedom
## Multiple R-squared: 0.7055, Adjusted R-squared: 0.7049
## F-statistic: 1188 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count 0.645175463936265
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9680 -0.6992 0.0258 0.8606 3.4855
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95763 0.08487 117.326 < 2e-16 ***
## category_code_LT01_2_count 0.43651 0.09505 4.592 5.58e-06 ***
## category_code_LT01_3_count 0.20832 0.11389 1.829 0.0680 .
## category_code_LT01_4_count 0.52843 0.10135 5.214 2.73e-07 ***
## category_code_LT01_5_count 0.90241 0.06009 15.019 < 2e-16 ***
## category_code_LT01_7_count 0.35361 0.15404 2.296 0.0221 *
## category_code_LT01_11_count 0.13823 0.12072 1.145 0.2527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.342 on 491 degrees of freedom
## Multiple R-squared: 0.6495, Adjusted R-squared: 0.6452
## F-statistic: 151.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count 0.644227974092509
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.6968 0.0141 0.8424 3.4895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.952845 0.084882 117.255 < 2e-16 ***
## category_code_LT01_2_count 0.472941 0.090495 5.226 2.56e-07 ***
## category_code_LT01_3_count 0.233328 0.112080 2.082 0.03788 *
## category_code_LT01_4_count 0.555890 0.098811 5.626 3.11e-08 ***
## category_code_LT01_5_count 0.903158 0.060400 14.953 < 2e-16 ***
## category_code_LT01_7_count 0.392788 0.150394 2.612 0.00928 **
## category_code_LT01_12_count -0.000639 0.200667 -0.003 0.99746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6485, Adjusted R-squared: 0.6442
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count 0.644228400838299
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9638 -0.6969 0.0139 0.8423 3.4895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.952849 0.084882 117.255 < 2e-16 ***
## category_code_LT01_2_count 0.472969 0.089740 5.270 2.04e-07 ***
## category_code_LT01_3_count 0.233327 0.111927 2.085 0.03762 *
## category_code_LT01_4_count 0.556021 0.098799 5.628 3.07e-08 ***
## category_code_LT01_5_count 0.903168 0.060172 15.010 < 2e-16 ***
## category_code_LT01_7_count 0.393211 0.151374 2.598 0.00967 **
## category_code_LT01_13_count -0.005818 0.237708 -0.024 0.98048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6485, Adjusted R-squared: 0.6442
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count 0.644231339785104
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.6964 0.0160 0.8424 3.4897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95310 0.08496 117.147 < 2e-16 ***
## category_code_LT01_2_count 0.47257 0.08984 5.260 2.15e-07 ***
## category_code_LT01_3_count 0.23373 0.11209 2.085 0.03757 *
## category_code_LT01_4_count 0.55498 0.09947 5.579 3.99e-08 ***
## category_code_LT01_5_count 0.90272 0.06047 14.928 < 2e-16 ***
## category_code_LT01_7_count 0.39212 0.15071 2.602 0.00955 **
## category_code_LT01_14_count 0.02177 0.31910 0.068 0.94563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6485, Adjusted R-squared: 0.6442
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count 0.644239284030414
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9635 -0.6980 0.0126 0.8418 3.4898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95273 0.08489 117.249 < 2e-16 ***
## category_code_LT01_2_count 0.47337 0.08978 5.273 2.02e-07 ***
## category_code_LT01_3_count 0.23501 0.11275 2.084 0.03764 *
## category_code_LT01_4_count 0.55646 0.09872 5.637 2.92e-08 ***
## category_code_LT01_5_count 0.90296 0.06018 15.004 < 2e-16 ***
## category_code_LT01_7_count 0.39208 0.15050 2.605 0.00946 **
## category_code_LT01_15_count -0.09166 0.73341 -0.125 0.90059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6485, Adjusted R-squared: 0.6442
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_16_count 0.644255797277551
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9639 -0.6955 0.0177 0.8422 3.4894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95311 0.08489 117.249 < 2e-16 ***
## category_code_LT01_2_count 0.47123 0.09010 5.230 2.51e-07 ***
## category_code_LT01_3_count 0.23107 0.11250 2.054 0.04051 *
## category_code_LT01_4_count 0.55708 0.09880 5.639 2.90e-08 ***
## category_code_LT01_5_count 0.90295 0.06017 15.007 < 2e-16 ***
## category_code_LT01_7_count 0.39326 0.15041 2.615 0.00921 **
## category_code_LT01_16_count 0.22374 1.14160 0.196 0.84470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count 0.640840339282294
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9658 -0.7393 0.0735 0.8656 3.4844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94975 0.08534 116.592 < 2e-16 ***
## category_code_LT01_2_count 0.49038 0.08985 5.458 7.67e-08 ***
## category_code_LT01_3_count 0.22114 0.11343 1.950 0.0518 .
## category_code_LT01_4_count 0.61141 0.09646 6.338 5.27e-10 ***
## category_code_LT01_5_count 0.91064 0.06117 14.888 < 2e-16 ***
## category_code_LT01_8_count -0.14574 0.26734 -0.545 0.5859
## category_code_LT01_9_count 0.30411 0.22213 1.369 0.1716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6452, Adjusted R-squared: 0.6408
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count 0.63975363939449
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9573 -0.7272 0.0453 0.8587 3.4147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93766 0.08852 112.269 < 2e-16 ***
## category_code_LT01_2_count 0.49998 0.08966 5.576 4.06e-08 ***
## category_code_LT01_3_count 0.22963 0.11422 2.011 0.0449 *
## category_code_LT01_4_count 0.61550 0.09655 6.375 4.23e-10 ***
## category_code_LT01_5_count 0.91581 0.06114 14.978 < 2e-16 ***
## category_code_LT01_8_count -0.13736 0.26767 -0.513 0.6081
## category_code_LT01_10_count 0.06926 0.11125 0.623 0.5339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6441, Adjusted R-squared: 0.6398
## F-statistic: 148.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count 0.641522226066339
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9770 -0.7474 0.0598 0.8401 3.4722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95911 0.08534 116.693 < 2e-16 ***
## category_code_LT01_2_count 0.44702 0.09543 4.684 3.64e-06 ***
## category_code_LT01_3_count 0.20450 0.11449 1.786 0.0747 .
## category_code_LT01_4_count 0.56813 0.10047 5.655 2.65e-08 ***
## category_code_LT01_5_count 0.91318 0.06101 14.967 < 2e-16 ***
## category_code_LT01_8_count -0.12302 0.26703 -0.461 0.6452
## category_code_LT01_11_count 0.19845 0.11834 1.677 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6415
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count 0.639469316290133
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9717 -0.7393 0.0648 0.8675 3.4765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9520418 0.0854843 116.419 < 2e-16 ***
## category_code_LT01_2_count 0.5036545 0.0903011 5.578 4.04e-08 ***
## category_code_LT01_3_count 0.2414331 0.1128218 2.140 0.0329 *
## category_code_LT01_4_count 0.6160858 0.0967885 6.365 4.48e-10 ***
## category_code_LT01_5_count 0.9157245 0.0613761 14.920 < 2e-16 ***
## category_code_LT01_8_count -0.1340313 0.2678986 -0.500 0.6171
## category_code_LT01_12_count 0.0008175 0.2021399 0.004 0.9968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6438, Adjusted R-squared: 0.6395
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count 0.639512392812952
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9713 -0.7391 0.0561 0.8678 3.4770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95198 0.08548 116.426 < 2e-16 ***
## category_code_LT01_2_count 0.50273 0.08958 5.612 3.35e-08 ***
## category_code_LT01_3_count 0.24117 0.11267 2.140 0.0328 *
## category_code_LT01_4_count 0.61396 0.09699 6.330 5.52e-10 ***
## category_code_LT01_5_count 0.91527 0.06120 14.956 < 2e-16 ***
## category_code_LT01_8_count -0.13024 0.26815 -0.486 0.6274
## category_code_LT01_13_count 0.05769 0.23812 0.242 0.8087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6395
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count 0.639513083136579
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9716 -0.7346 0.0676 0.8675 3.4772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95298 0.08556 116.321 < 2e-16 ***
## category_code_LT01_2_count 0.50229 0.08968 5.601 3.55e-08 ***
## category_code_LT01_3_count 0.24294 0.11283 2.153 0.0318 *
## category_code_LT01_4_count 0.61255 0.09768 6.271 7.86e-10 ***
## category_code_LT01_5_count 0.91423 0.06148 14.871 < 2e-16 ***
## category_code_LT01_8_count -0.13505 0.26774 -0.504 0.6142
## category_code_LT01_14_count 0.07828 0.32057 0.244 0.8072
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6395
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count 0.639504161070955
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9712 -0.7398 0.0723 0.8679 3.4771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95184 0.08548 116.417 < 2e-16 ***
## category_code_LT01_2_count 0.50443 0.08955 5.633 2.99e-08 ***
## category_code_LT01_3_count 0.24441 0.11348 2.154 0.0317 *
## category_code_LT01_4_count 0.61695 0.09666 6.383 4.03e-10 ***
## category_code_LT01_5_count 0.91538 0.06119 14.960 < 2e-16 ***
## category_code_LT01_8_count -0.13346 0.26771 -0.499 0.6183
## category_code_LT01_15_count -0.16075 0.73777 -0.218 0.8276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6395
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_16_count 0.639492473539768
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9719 -0.7396 0.0652 0.8673 3.4763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95230 0.08549 116.409 < 2e-16 ***
## category_code_LT01_2_count 0.50221 0.08989 5.587 3.83e-08 ***
## category_code_LT01_3_count 0.23944 0.11324 2.115 0.035 *
## category_code_LT01_4_count 0.61730 0.09681 6.376 4.20e-10 ***
## category_code_LT01_5_count 0.91566 0.06117 14.969 < 2e-16 ***
## category_code_LT01_8_count -0.13632 0.26803 -0.509 0.611
## category_code_LT01_16_count 0.20436 1.15045 0.178 0.859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6438, Adjusted R-squared: 0.6395
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count 0.640794167548407
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9500 -0.7268 0.0363 0.8649 3.4428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93720 0.08836 112.458 < 2e-16 ***
## category_code_LT01_2_count 0.48860 0.08999 5.429 8.91e-08 ***
## category_code_LT01_3_count 0.21143 0.11478 1.842 0.0661 .
## category_code_LT01_4_count 0.61037 0.09646 6.327 5.62e-10 ***
## category_code_LT01_5_count 0.90589 0.06051 14.972 < 2e-16 ***
## category_code_LT01_9_count 0.28971 0.22309 1.299 0.1947
## category_code_LT01_10_count 0.05399 0.11160 0.484 0.6288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6408
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.703306291088506
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7620 -0.6629 0.0302 0.6375 3.9139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.46419 0.08355 113.28 <2e-16 ***
## new_category_count_col 1.31701 0.03835 34.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.227 on 496 degrees of freedom
## Multiple R-squared: 0.7039, Adjusted R-squared: 0.7033
## F-statistic: 1179 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count 0.642615804882538
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9668 -0.7388 0.0366 0.8272 3.4862
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95549 0.08520 116.852 < 2e-16 ***
## category_code_LT01_2_count 0.43558 0.09568 4.552 6.70e-06 ***
## category_code_LT01_3_count 0.18433 0.11516 1.601 0.1101
## category_code_LT01_4_count 0.56363 0.10033 5.618 3.25e-08 ***
## category_code_LT01_5_count 0.90376 0.06036 14.972 < 2e-16 ***
## category_code_LT01_9_count 0.29017 0.22155 1.310 0.1909
## category_code_LT01_11_count 0.19554 0.11817 1.655 0.0986 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6469, Adjusted R-squared: 0.6426
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count 0.64062301649117
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9611 -0.7365 0.0407 0.8706 3.4911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.948349 0.085325 116.593 < 2e-16 ***
## category_code_LT01_2_count 0.491150 0.090672 5.417 9.52e-08 ***
## category_code_LT01_3_count 0.220024 0.113602 1.937 0.0533 .
## category_code_LT01_4_count 0.610755 0.096690 6.317 6.00e-10 ***
## category_code_LT01_5_count 0.905790 0.060760 14.908 < 2e-16 ***
## category_code_LT01_9_count 0.300216 0.222087 1.352 0.1771
## category_code_LT01_12_count -0.001956 0.201680 -0.010 0.9923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.645, Adjusted R-squared: 0.6406
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count 0.640718605442336
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9605 -0.7364 0.0377 0.8774 3.4919
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94827 0.08531 116.608 < 2e-16 ***
## category_code_LT01_2_count 0.48933 0.08998 5.438 8.51e-08 ***
## category_code_LT01_3_count 0.21923 0.11345 1.932 0.0539 .
## category_code_LT01_4_count 0.60744 0.09689 6.269 7.95e-10 ***
## category_code_LT01_5_count 0.90513 0.06054 14.952 < 2e-16 ***
## category_code_LT01_9_count 0.30555 0.22254 1.373 0.1704
## category_code_LT01_13_count 0.08600 0.23785 0.362 0.7178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6407
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count 0.640637752805786
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9610 -0.7368 0.0473 0.8750 3.4915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94890 0.08541 116.481 < 2e-16 ***
## category_code_LT01_2_count 0.49031 0.09001 5.447 8.11e-08 ***
## category_code_LT01_3_count 0.22097 0.11366 1.944 0.0525 .
## category_code_LT01_4_count 0.60865 0.09755 6.240 9.49e-10 ***
## category_code_LT01_5_count 0.90488 0.06082 14.877 < 2e-16 ***
## category_code_LT01_9_count 0.29801 0.22263 1.339 0.1813
## category_code_LT01_14_count 0.04563 0.32081 0.142 0.8870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.645, Adjusted R-squared: 0.6406
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count 0.640643438526409
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9607 -0.7363 0.0525 0.8627 3.4915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94821 0.08533 116.590 < 2e-16 ***
## category_code_LT01_2_count 0.49166 0.08995 5.466 7.33e-08 ***
## category_code_LT01_3_count 0.22234 0.11432 1.945 0.0524 .
## category_code_LT01_4_count 0.61137 0.09656 6.331 5.50e-10 ***
## category_code_LT01_5_count 0.90550 0.06054 14.958 < 2e-16 ***
## category_code_LT01_9_count 0.29870 0.22227 1.344 0.1796
## category_code_LT01_15_count -0.12335 0.73720 -0.167 0.8672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.645, Adjusted R-squared: 0.6406
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_16_count 0.64063517726849
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9611 -0.7366 0.0443 0.8719 3.4911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94852 0.08533 116.583 < 2e-16 ***
## category_code_LT01_2_count 0.48997 0.09024 5.429 8.90e-08 ***
## category_code_LT01_3_count 0.21852 0.11399 1.917 0.0558 .
## category_code_LT01_4_count 0.61156 0.09671 6.323 5.76e-10 ***
## category_code_LT01_5_count 0.90563 0.06053 14.962 < 2e-16 ***
## category_code_LT01_9_count 0.29972 0.22212 1.349 0.1778
## category_code_LT01_16_count 0.14832 1.14742 0.129 0.8972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.645, Adjusted R-squared: 0.6406
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count 0.641644033008117
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9586 -0.7333 0.0381 0.8507 3.4172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94373 0.08836 112.537 < 2e-16 ***
## category_code_LT01_2_count 0.44336 0.09560 4.638 4.52e-06 ***
## category_code_LT01_3_count 0.19133 0.11600 1.649 0.0997 .
## category_code_LT01_4_count 0.56651 0.10044 5.640 2.87e-08 ***
## category_code_LT01_5_count 0.90892 0.06032 15.069 < 2e-16 ***
## category_code_LT01_10_count 0.06832 0.11094 0.616 0.5383
## category_code_LT01_11_count 0.19986 0.11829 1.690 0.0917 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.646, Adjusted R-squared: 0.6416
## F-statistic: 149.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count 0.639561166036288
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9531 -0.7261 0.0441 0.8646 3.4219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93654 0.08851 112.259 < 2e-16 ***
## category_code_LT01_2_count 0.50087 0.09045 5.537 5.01e-08 ***
## category_code_LT01_3_count 0.22865 0.11435 1.999 0.0461 *
## category_code_LT01_4_count 0.61498 0.09677 6.355 4.77e-10 ***
## category_code_LT01_5_count 0.91130 0.06071 15.010 < 2e-16 ***
## category_code_LT01_10_count 0.06820 0.11130 0.613 0.5403
## category_code_LT01_12_count -0.00639 0.20206 -0.032 0.9748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6396
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count 0.639608280576586
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9529 -0.7262 0.0344 0.8680 3.4233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93668 0.08851 112.267 < 2e-16 ***
## category_code_LT01_2_count 0.49950 0.08976 5.565 4.32e-08 ***
## category_code_LT01_3_count 0.22834 0.11422 1.999 0.0461 *
## category_code_LT01_4_count 0.61254 0.09696 6.318 5.96e-10 ***
## category_code_LT01_5_count 0.91077 0.06049 15.057 < 2e-16 ***
## category_code_LT01_10_count 0.06738 0.11129 0.605 0.5451
## category_code_LT01_13_count 0.06071 0.23777 0.255 0.7986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.6396
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count 0.639568102878823
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9535 -0.7270 0.0378 0.8653 3.4248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93749 0.08899 111.674 < 2e-16 ***
## category_code_LT01_2_count 0.50003 0.08979 5.569 4.23e-08 ***
## category_code_LT01_3_count 0.22956 0.11471 2.001 0.0459 *
## category_code_LT01_4_count 0.61327 0.09769 6.278 7.56e-10 ***
## category_code_LT01_5_count 0.91047 0.06082 14.969 < 2e-16 ***
## category_code_LT01_10_count 0.06551 0.11413 0.574 0.5663
## category_code_LT01_14_count 0.03361 0.32877 0.102 0.9186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6396
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count 0.639609579052015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9521 -0.7255 0.0309 0.8642 3.4214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93597 0.08854 112.225 < 2e-16 ***
## category_code_LT01_2_count 0.50127 0.08972 5.587 3.84e-08 ***
## category_code_LT01_3_count 0.23170 0.11490 2.017 0.0443 *
## category_code_LT01_4_count 0.61577 0.09664 6.372 4.30e-10 ***
## category_code_LT01_5_count 0.91072 0.06049 15.055 < 2e-16 ***
## category_code_LT01_10_count 0.06980 0.11144 0.626 0.5314
## category_code_LT01_15_count -0.19120 0.73890 -0.259 0.7959
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.6396
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_16_count 0.639574730670092
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9531 -0.7264 0.0446 0.8648 3.4225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93681 0.08853 112.241 < 2e-16 ***
## category_code_LT01_2_count 0.49934 0.09006 5.545 4.81e-08 ***
## category_code_LT01_3_count 0.22694 0.11476 1.978 0.0485 *
## category_code_LT01_4_count 0.61571 0.09679 6.361 4.60e-10 ***
## category_code_LT01_5_count 0.91100 0.06048 15.062 < 2e-16 ***
## category_code_LT01_10_count 0.06776 0.11128 0.609 0.5429
## category_code_LT01_16_count 0.16039 1.14921 0.140 0.8891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6396
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count 0.641493131134362
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9749 -0.7354 0.0348 0.8428 3.4751
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95849 0.08532 116.718 < 2e-16 ***
## category_code_LT01_2_count 0.44882 0.09553 4.698 3.41e-06 ***
## category_code_LT01_3_count 0.20340 0.11446 1.777 0.0762 .
## category_code_LT01_4_count 0.56702 0.10045 5.645 2.80e-08 ***
## category_code_LT01_5_count 0.91112 0.06055 15.047 < 2e-16 ***
## category_code_LT01_11_count 0.21143 0.12159 1.739 0.0827 .
## category_code_LT01_12_count -0.08595 0.20702 -0.415 0.6782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6415
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count 0.641396118245001
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9726 -0.7421 0.0342 0.8388 3.4784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95789 0.08532 116.709 < 2e-16 ***
## category_code_LT01_2_count 0.44653 0.09548 4.677 3.77e-06 ***
## category_code_LT01_3_count 0.20302 0.11447 1.774 0.0767 .
## category_code_LT01_4_count 0.56566 0.10075 5.615 3.30e-08 ***
## category_code_LT01_5_count 0.90870 0.06035 15.057 < 2e-16 ***
## category_code_LT01_11_count 0.19877 0.11844 1.678 0.0939 .
## category_code_LT01_13_count 0.04718 0.23733 0.199 0.8425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6457, Adjusted R-squared: 0.6414
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.701122061715582
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7828 -0.6306 0.0296 0.6642 3.8887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.48936 0.08337 113.82 <2e-16 ***
## new_category_count_col 1.31070 0.03837 34.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.231 on 496 degrees of freedom
## Multiple R-squared: 0.7017, Adjusted R-squared: 0.7011
## F-statistic: 1167 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count 0.641398609279791
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9727 -0.7416 0.0447 0.8345 3.4787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95871 0.08540 116.610 < 2e-16 ***
## category_code_LT01_2_count 0.44598 0.09558 4.666 3.97e-06 ***
## category_code_LT01_3_count 0.20437 0.11465 1.783 0.0753 .
## category_code_LT01_4_count 0.56424 0.10145 5.562 4.39e-08 ***
## category_code_LT01_5_count 0.90766 0.06066 14.962 < 2e-16 ***
## category_code_LT01_11_count 0.19935 0.11835 1.684 0.0927 .
## category_code_LT01_14_count 0.06624 0.31974 0.207 0.8360
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6457, Adjusted R-squared: 0.6414
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count 0.641416331221223
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9724 -0.7423 0.0267 0.8463 3.4787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95774 0.08532 116.706 < 2e-16 ***
## category_code_LT01_2_count 0.44772 0.09548 4.689 3.56e-06 ***
## category_code_LT01_3_count 0.20643 0.11521 1.792 0.0738 .
## category_code_LT01_4_count 0.56799 0.10052 5.651 2.71e-08 ***
## category_code_LT01_5_count 0.90855 0.06036 15.053 < 2e-16 ***
## category_code_LT01_11_count 0.20044 0.11835 1.694 0.0910 .
## category_code_LT01_15_count -0.19077 0.73595 -0.259 0.7956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6457, Adjusted R-squared: 0.6414
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_16_count 0.641395904877713
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9730 -0.7425 0.0287 0.8455 3.4779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95823 0.08533 116.696 < 2e-16 ***
## category_code_LT01_2_count 0.44521 0.09589 4.643 4.42e-06 ***
## category_code_LT01_3_count 0.20066 0.11510 1.743 0.0819 .
## category_code_LT01_4_count 0.56831 0.10064 5.647 2.76e-08 ***
## category_code_LT01_5_count 0.90877 0.06034 15.060 < 2e-16 ***
## category_code_LT01_11_count 0.20040 0.11837 1.693 0.0911 .
## category_code_LT01_16_count 0.22704 1.14643 0.198 0.8431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6457, Adjusted R-squared: 0.6414
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count 0.639339491731101
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9670 -0.7367 0.0628 0.8727 3.4831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.950703 0.085459 116.438 < 2e-16 ***
## category_code_LT01_2_count 0.503294 0.090387 5.568 4.24e-08 ***
## category_code_LT01_3_count 0.239976 0.112820 2.127 0.0339 *
## category_code_LT01_4_count 0.613147 0.097184 6.309 6.27e-10 ***
## category_code_LT01_5_count 0.910901 0.060747 14.995 < 2e-16 ***
## category_code_LT01_12_count -0.004126 0.202088 -0.020 0.9837
## category_code_LT01_13_count 0.064470 0.237843 0.271 0.7865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count 0.639326997589142
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9672 -0.7254 0.0725 0.8727 3.4834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.951634 0.085546 116.331 < 2e-16 ***
## category_code_LT01_2_count 0.503155 0.090442 5.563 4.36e-08 ***
## category_code_LT01_3_count 0.241763 0.113004 2.139 0.0329 *
## category_code_LT01_4_count 0.612115 0.097836 6.257 8.58e-10 ***
## category_code_LT01_5_count 0.909837 0.061026 14.909 < 2e-16 ***
## category_code_LT01_12_count -0.006373 0.202566 -0.031 0.9749
## category_code_LT01_14_count 0.076381 0.321444 0.238 0.8123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count 0.639322006839745
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9668 -0.7333 0.0719 0.8729 3.4832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95053 0.08547 116.427 < 2e-16 ***
## category_code_LT01_2_count 0.50514 0.09038 5.589 3.80e-08 ***
## category_code_LT01_3_count 0.24329 0.11365 2.141 0.0328 *
## category_code_LT01_4_count 0.61639 0.09689 6.362 4.57e-10 ***
## category_code_LT01_5_count 0.91093 0.06075 14.994 < 2e-16 ***
## category_code_LT01_12_count -0.00424 0.20213 -0.021 0.9833
## category_code_LT01_15_count -0.16453 0.73825 -0.223 0.8237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_16_count 0.639302630041008
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9673 -0.7301 0.0690 0.8723 3.4827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.950932 0.085474 116.420 < 2e-16 ***
## category_code_LT01_2_count 0.503002 0.090725 5.544 4.83e-08 ***
## category_code_LT01_3_count 0.238452 0.113416 2.102 0.036 *
## category_code_LT01_4_count 0.616477 0.097020 6.354 4.79e-10 ***
## category_code_LT01_5_count 0.911092 0.060746 14.998 < 2e-16 ***
## category_code_LT01_12_count -0.002237 0.202098 -0.011 0.991
## category_code_LT01_16_count 0.175443 1.149645 0.153 0.879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count 0.639380398764435
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9667 -0.7366 0.0613 0.8732 3.4840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95159 0.08554 116.342 < 2e-16 ***
## category_code_LT01_2_count 0.50168 0.08978 5.588 3.81e-08 ***
## category_code_LT01_3_count 0.24129 0.11282 2.139 0.033 *
## category_code_LT01_4_count 0.60955 0.09808 6.215 1.10e-09 ***
## category_code_LT01_5_count 0.90929 0.06084 14.946 < 2e-16 ***
## category_code_LT01_13_count 0.06455 0.23777 0.271 0.786
## category_code_LT01_14_count 0.07594 0.32059 0.237 0.813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count 0.639369962447684
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9665 -0.7364 0.0677 0.8733 3.4838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95052 0.08546 116.435 < 2e-16 ***
## category_code_LT01_2_count 0.50379 0.08967 5.618 3.23e-08 ***
## category_code_LT01_3_count 0.24265 0.11349 2.138 0.033 *
## category_code_LT01_4_count 0.61394 0.09709 6.323 5.76e-10 ***
## category_code_LT01_5_count 0.91048 0.06053 15.042 < 2e-16 ***
## category_code_LT01_13_count 0.06110 0.23831 0.256 0.798
## category_code_LT01_15_count -0.15138 0.73954 -0.205 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_16_count 0.639358934738484
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9670 -0.7368 0.0638 0.8727 3.4832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95092 0.08547 116.430 < 2e-16 ***
## category_code_LT01_2_count 0.50165 0.09000 5.574 4.12e-08 ***
## category_code_LT01_3_count 0.23797 0.11325 2.101 0.0361 *
## category_code_LT01_4_count 0.61405 0.09719 6.318 5.95e-10 ***
## category_code_LT01_5_count 0.91063 0.06052 15.047 < 2e-16 ***
## category_code_LT01_13_count 0.06594 0.23797 0.277 0.7818
## category_code_LT01_16_count 0.18861 1.15022 0.164 0.8698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count 0.639363725323981
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9666 -0.7246 0.0752 0.8734 3.4841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95143 0.08554 116.332 < 2e-16 ***
## category_code_LT01_2_count 0.50352 0.08975 5.610 3.38e-08 ***
## category_code_LT01_3_count 0.24465 0.11364 2.153 0.0318 *
## category_code_LT01_4_count 0.61278 0.09776 6.268 8.00e-10 ***
## category_code_LT01_5_count 0.90930 0.06085 14.944 < 2e-16 ***
## category_code_LT01_14_count 0.07671 0.32063 0.239 0.8110
## category_code_LT01_15_count -0.16665 0.73796 -0.226 0.8214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_16_count 0.639346226461766
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9671 -0.7253 0.0744 0.8728 3.4835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95187 0.08556 116.321 < 2e-16 ***
## category_code_LT01_2_count 0.50136 0.09011 5.564 4.34e-08 ***
## category_code_LT01_3_count 0.23972 0.11337 2.114 0.035 *
## category_code_LT01_4_count 0.61292 0.09787 6.263 8.27e-10 ***
## category_code_LT01_5_count 0.90947 0.06084 14.949 < 2e-16 ***
## category_code_LT01_14_count 0.07828 0.32100 0.244 0.807
## category_code_LT01_16_count 0.18967 1.15072 0.165 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_15_count+category_code_LT01_16_count 0.639336582166296
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9669 -0.7366 0.0731 0.8729 3.4833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95072 0.08548 116.417 < 2e-16 ***
## category_code_LT01_2_count 0.50367 0.08998 5.598 3.62e-08 ***
## category_code_LT01_3_count 0.24143 0.11412 2.116 0.0349 *
## category_code_LT01_4_count 0.61718 0.09689 6.370 4.35e-10 ***
## category_code_LT01_5_count 0.91070 0.06052 15.047 < 2e-16 ***
## category_code_LT01_15_count -0.15904 0.73876 -0.215 0.8296
## category_code_LT01_16_count 0.16387 1.15063 0.142 0.8868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.627632948101476
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9934 -0.7492 0.0132 0.8562 3.8389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96204 0.08693 114.603 < 2e-16 ***
## category_code_LT01_2_count 0.65637 0.08314 7.895 1.91e-14 ***
## category_code_LT01_3_count 0.40735 0.10874 3.746 0.000201 ***
## category_code_LT01_5_count 0.93271 0.06211 15.016 < 2e-16 ***
## category_code_LT01_6_count 0.43249 0.15112 2.862 0.004392 **
## category_code_LT01_7_count 0.58175 0.14983 3.883 0.000117 ***
## category_code_LT01_8_count -0.17868 0.27244 -0.656 0.512239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6321, Adjusted R-squared: 0.6276
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.628191760936863
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9831 -0.7480 0.0115 0.8837 3.8425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95849 0.08684 114.674 < 2e-16 ***
## category_code_LT01_2_count 0.64862 0.08348 7.770 4.63e-14 ***
## category_code_LT01_3_count 0.38960 0.10968 3.552 0.000419 ***
## category_code_LT01_5_count 0.92289 0.06153 14.999 < 2e-16 ***
## category_code_LT01_6_count 0.42246 0.15098 2.798 0.005343 **
## category_code_LT01_7_count 0.56300 0.15030 3.746 0.000201 ***
## category_code_LT01_9_count 0.24533 0.22693 1.081 0.280196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6327, Adjusted R-squared: 0.6282
## F-statistic: 141 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.627306768520716
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9878 -0.7487 0.0034 0.8684 3.8405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9604766 0.0899649 110.715 < 2e-16 ***
## category_code_LT01_2_count 0.6577809 0.0831511 7.911 1.71e-14 ***
## category_code_LT01_3_count 0.4059355 0.1101165 3.686 0.000253 ***
## category_code_LT01_5_count 0.9268536 0.0615086 15.069 < 2e-16 ***
## category_code_LT01_6_count 0.4285014 0.1525369 2.809 0.005165 **
## category_code_LT01_7_count 0.5784321 0.1501549 3.852 0.000133 ***
## category_code_LT01_10_count -0.0004876 0.1145235 -0.004 0.996604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.630370518536854
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9936 -0.7578 0.0659 0.8703 3.8326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96835 0.08666 115.029 < 2e-16 ***
## category_code_LT01_2_count 0.57854 0.09165 6.313 6.14e-10 ***
## category_code_LT01_3_count 0.34728 0.11215 3.097 0.00207 **
## category_code_LT01_5_count 0.92380 0.06126 15.080 < 2e-16 ***
## category_code_LT01_6_count 0.38528 0.15195 2.536 0.01153 *
## category_code_LT01_7_count 0.49322 0.15505 3.181 0.00156 **
## category_code_LT01_11_count 0.24394 0.12092 2.017 0.04420 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6348, Adjusted R-squared: 0.6304
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.627309017654878
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9875 -0.7487 0.0054 0.8676 3.8406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96038 0.08693 114.583 < 2e-16 ***
## category_code_LT01_2_count 0.65710 0.08407 7.816 3.34e-14 ***
## category_code_LT01_3_count 0.40548 0.10900 3.720 0.000222 ***
## category_code_LT01_5_count 0.92657 0.06172 15.013 < 2e-16 ***
## category_code_LT01_6_count 0.42754 0.15190 2.815 0.005078 **
## category_code_LT01_7_count 0.57832 0.14981 3.860 0.000128 ***
## category_code_LT01_12_count 0.01125 0.20609 0.055 0.956479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.627397506122662
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9871 -0.7479 0.0077 0.8710 3.8407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96021 0.08692 114.593 < 2e-16 ***
## category_code_LT01_2_count 0.65570 0.08335 7.867 2.34e-14 ***
## category_code_LT01_3_count 0.40472 0.10881 3.720 0.000222 ***
## category_code_LT01_5_count 0.92628 0.06151 15.059 < 2e-16 ***
## category_code_LT01_6_count 0.42889 0.15105 2.839 0.004708 **
## category_code_LT01_7_count 0.57153 0.15109 3.783 0.000174 ***
## category_code_LT01_13_count 0.08396 0.24280 0.346 0.729630
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.628067078113434
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9852 -0.7277 0.0084 0.8612 3.8377
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96328 0.08689 114.669 < 2e-16 ***
## category_code_LT01_2_count 0.64428 0.08415 7.657 1.02e-13 ***
## category_code_LT01_3_count 0.40601 0.10866 3.737 0.000208 ***
## category_code_LT01_5_count 0.91900 0.06193 14.839 < 2e-16 ***
## category_code_LT01_6_count 0.43957 0.15132 2.905 0.003839 **
## category_code_LT01_7_count 0.56347 0.15039 3.747 0.000200 ***
## category_code_LT01_14_count 0.32493 0.32432 1.002 0.316904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6326, Adjusted R-squared: 0.6281
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.627307375245927
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9878 -0.7488 -0.0035 0.8686 3.8406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96040 0.08693 114.576 < 2e-16 ***
## category_code_LT01_2_count 0.65763 0.08330 7.895 1.91e-14 ***
## category_code_LT01_3_count 0.40543 0.10982 3.692 0.000248 ***
## category_code_LT01_5_count 0.92690 0.06151 15.069 < 2e-16 ***
## category_code_LT01_6_count 0.42824 0.15118 2.833 0.004806 **
## category_code_LT01_7_count 0.57851 0.14987 3.860 0.000128 ***
## category_code_LT01_15_count 0.02145 0.75039 0.029 0.977202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.627325377270462
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9877 -0.7489 0.0056 0.8687 3.8404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96055 0.08693 114.578 < 2e-16 ***
## category_code_LT01_2_count 0.65639 0.08361 7.851 2.62e-14 ***
## category_code_LT01_3_count 0.40416 0.10931 3.697 0.000242 ***
## category_code_LT01_5_count 0.92665 0.06151 15.065 < 2e-16 ***
## category_code_LT01_6_count 0.43102 0.15197 2.836 0.004754 **
## category_code_LT01_7_count 0.57907 0.14986 3.864 0.000127 ***
## category_code_LT01_16_count 0.18376 1.17319 0.157 0.875595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.617811744466402
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9992 -0.7548 0.0354 0.8717 3.8419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95909 0.08808 113.067 < 2e-16 ***
## category_code_LT01_2_count 0.73157 0.08155 8.971 < 2e-16 ***
## category_code_LT01_3_count 0.42866 0.11075 3.871 0.000123 ***
## category_code_LT01_5_count 0.94531 0.06287 15.036 < 2e-16 ***
## category_code_LT01_6_count 0.43774 0.15317 2.858 0.004446 **
## category_code_LT01_8_count -0.15487 0.27599 -0.561 0.574954
## category_code_LT01_9_count 0.32981 0.22916 1.439 0.150722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.616253575300738
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9995 -0.7682 0.0061 0.8839 3.8456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95534 0.09130 109.038 < 2e-16 ***
## category_code_LT01_2_count 0.74647 0.08103 9.212 < 2e-16 ***
## category_code_LT01_3_count 0.44722 0.11127 4.019 6.75e-05 ***
## category_code_LT01_5_count 0.95121 0.06288 15.126 < 2e-16 ***
## category_code_LT01_6_count 0.44028 0.15489 2.843 0.00466 **
## category_code_LT01_8_count -0.14360 0.27644 -0.519 0.60368
## category_code_LT01_10_count 0.03053 0.11595 0.263 0.79244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6163
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.622904180643884
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0104 -0.7697 0.0433 0.8366 3.8282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97272 0.08756 113.897 < 2e-16 ***
## category_code_LT01_2_count 0.61555 0.09182 6.704 5.58e-11 ***
## category_code_LT01_3_count 0.35900 0.11328 3.169 0.00162 **
## category_code_LT01_5_count 0.94183 0.06240 15.092 < 2e-16 ***
## category_code_LT01_6_count 0.38109 0.15362 2.481 0.01344 *
## category_code_LT01_8_count -0.12187 0.27410 -0.445 0.65679
## category_code_LT01_11_count 0.34735 0.11756 2.955 0.00328 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6275, Adjusted R-squared: 0.6229
## F-statistic: 137.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.616207665856987
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0052 -0.7599 0.0209 0.8738 3.8394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96152 0.08825 112.878 < 2e-16 ***
## category_code_LT01_2_count 0.74585 0.08201 9.095 < 2e-16 ***
## category_code_LT01_3_count 0.45126 0.11002 4.102 4.8e-05 ***
## category_code_LT01_5_count 0.95041 0.06308 15.066 < 2e-16 ***
## category_code_LT01_6_count 0.44432 0.15421 2.881 0.00413 **
## category_code_LT01_8_count -0.14349 0.27659 -0.519 0.60414
## category_code_LT01_12_count 0.02152 0.20925 0.103 0.91813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6162
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.616711616328064
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0034 -0.7584 0.0188 0.8857 3.8399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96101 0.08819 112.944 < 2e-16 ***
## category_code_LT01_2_count 0.73980 0.08145 9.083 < 2e-16 ***
## category_code_LT01_3_count 0.44793 0.10982 4.079 5.28e-05 ***
## category_code_LT01_5_count 0.94860 0.06290 15.080 < 2e-16 ***
## category_code_LT01_6_count 0.44639 0.15328 2.912 0.00375 **
## category_code_LT01_8_count -0.13000 0.27668 -0.470 0.63867
## category_code_LT01_13_count 0.19806 0.24451 0.810 0.41831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.617665280418564
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0018 -0.7634 -0.0130 0.8868 3.8353
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96561 0.08813 113.075 < 2e-16 ***
## category_code_LT01_2_count 0.72526 0.08240 8.802 < 2e-16 ***
## category_code_LT01_3_count 0.45068 0.10958 4.113 4.58e-05 ***
## category_code_LT01_5_count 0.93965 0.06330 14.845 < 2e-16 ***
## category_code_LT01_6_count 0.46107 0.15349 3.004 0.0028 **
## category_code_LT01_8_count -0.15061 0.27597 -0.546 0.5855
## category_code_LT01_14_count 0.44905 0.32728 1.372 0.1707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.616203962196
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0054 -0.7622 0.0172 0.8752 3.8395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96144 0.08826 112.870 < 2e-16 ***
## category_code_LT01_2_count 0.74751 0.08112 9.215 < 2e-16 ***
## category_code_LT01_3_count 0.45315 0.11081 4.089 5.05e-05 ***
## category_code_LT01_5_count 0.95080 0.06290 15.116 < 2e-16 ***
## category_code_LT01_6_count 0.44642 0.15350 2.908 0.0038 **
## category_code_LT01_8_count -0.14237 0.27644 -0.515 0.6068
## category_code_LT01_15_count -0.05816 0.76120 -0.076 0.9391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6162
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.616203414378959
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0056 -0.7612 0.0186 0.8762 3.8394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96160 0.08826 112.868 < 2e-16 ***
## category_code_LT01_2_count 0.74657 0.08143 9.169 < 2e-16 ***
## category_code_LT01_3_count 0.45124 0.11029 4.091 5.01e-05 ***
## category_code_LT01_5_count 0.95088 0.06288 15.121 < 2e-16 ***
## category_code_LT01_6_count 0.44721 0.15436 2.897 0.00393 **
## category_code_LT01_8_count -0.14358 0.27682 -0.519 0.60421
## category_code_LT01_16_count 0.08541 1.19173 0.072 0.94290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6162
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.617578626424732
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9915 -0.7674 0.0360 0.8816 3.8462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95478 0.09112 109.252 < 2e-16 ***
## category_code_LT01_2_count 0.73233 0.08157 8.978 < 2e-16 ***
## category_code_LT01_3_count 0.42535 0.11201 3.797 0.000165 ***
## category_code_LT01_5_count 0.94038 0.06224 15.108 < 2e-16 ***
## category_code_LT01_6_count 0.43161 0.15455 2.793 0.005431 **
## category_code_LT01_9_count 0.32316 0.23012 1.404 0.160864
## category_code_LT01_10_count 0.01442 0.11624 0.124 0.901331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.624130556214407
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0002 -0.7587 0.0688 0.8817 3.8318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96912 0.08740 114.060 < 2e-16 ***
## category_code_LT01_2_count 0.60316 0.09214 6.546 1.49e-10 ***
## category_code_LT01_3_count 0.33708 0.11406 2.955 0.00327 **
## category_code_LT01_5_count 0.93228 0.06175 15.097 < 2e-16 ***
## category_code_LT01_6_count 0.37105 0.15330 2.420 0.01586 *
## category_code_LT01_9_count 0.30493 0.22726 1.342 0.18029
## category_code_LT01_11_count 0.34374 0.11739 2.928 0.00357 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.617573388825193
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9939 -0.7714 0.0330 0.8793 3.8433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95768 0.08807 113.062 < 2e-16 ***
## category_code_LT01_2_count 0.73133 0.08256 8.858 < 2e-16 ***
## category_code_LT01_3_count 0.42674 0.11100 3.844 0.000137 ***
## category_code_LT01_5_count 0.93972 0.06246 15.045 < 2e-16 ***
## category_code_LT01_6_count 0.43273 0.15394 2.811 0.005136 **
## category_code_LT01_9_count 0.32593 0.22912 1.423 0.155510
## category_code_LT01_12_count 0.01943 0.20876 0.093 0.925886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.618239225744238
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9920 -0.7505 0.0359 0.8851 3.8438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95713 0.08800 113.151 < 2e-16 ***
## category_code_LT01_2_count 0.72339 0.08208 8.814 < 2e-16 ***
## category_code_LT01_3_count 0.42196 0.11082 3.808 0.000158 ***
## category_code_LT01_5_count 0.93779 0.06223 15.070 < 2e-16 ***
## category_code_LT01_6_count 0.43472 0.15296 2.842 0.004668 **
## category_code_LT01_9_count 0.33915 0.22937 1.479 0.139878
## category_code_LT01_13_count 0.22705 0.24412 0.930 0.352789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.618791747856329
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9911 -0.7511 0.0290 0.8844 3.8394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96154 0.08799 113.217 < 2e-16 ***
## category_code_LT01_2_count 0.71354 0.08282 8.616 < 2e-16 ***
## category_code_LT01_3_count 0.42773 0.11058 3.868 0.000125 ***
## category_code_LT01_5_count 0.93003 0.06266 14.844 < 2e-16 ***
## category_code_LT01_6_count 0.44849 0.15327 2.926 0.003590 **
## category_code_LT01_9_count 0.30349 0.22944 1.323 0.186542
## category_code_LT01_14_count 0.41166 0.32771 1.256 0.209654
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6188
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.617567023981205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9942 -0.7658 0.0317 0.8781 3.8433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95766 0.08808 113.055 < 2e-16 ***
## category_code_LT01_2_count 0.73264 0.08170 8.967 < 2e-16 ***
## category_code_LT01_3_count 0.42777 0.11187 3.824 0.000148 ***
## category_code_LT01_5_count 0.94018 0.06224 15.105 < 2e-16 ***
## category_code_LT01_6_count 0.43437 0.15322 2.835 0.004771 **
## category_code_LT01_9_count 0.32561 0.22931 1.420 0.156264
## category_code_LT01_15_count -0.01684 0.76047 -0.022 0.982345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.617566856628874
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9943 -0.7650 0.0316 0.8784 3.8433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95770 0.08808 113.051 < 2e-16 ***
## category_code_LT01_2_count 0.73239 0.08195 8.937 < 2e-16 ***
## category_code_LT01_3_count 0.42725 0.11124 3.841 0.000139 ***
## category_code_LT01_5_count 0.94020 0.06224 15.106 < 2e-16 ***
## category_code_LT01_6_count 0.43452 0.15404 2.821 0.004984 **
## category_code_LT01_9_count 0.32574 0.22916 1.421 0.155822
## category_code_LT01_16_count 0.01972 1.18818 0.017 0.986768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.622827350878261
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7681 0.0480 0.8480 3.8366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96438 0.09055 110.038 < 2e-16 ***
## category_code_LT01_2_count 0.61458 0.09189 6.688 6.16e-11 ***
## category_code_LT01_3_count 0.35163 0.11476 3.064 0.0023 **
## category_code_LT01_5_count 0.93800 0.06173 15.195 < 2e-16 ***
## category_code_LT01_6_count 0.37116 0.15503 2.394 0.0170 *
## category_code_LT01_10_count 0.03592 0.11496 0.312 0.7548
## category_code_LT01_11_count 0.34936 0.11755 2.972 0.0031 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6274, Adjusted R-squared: 0.6228
## F-statistic: 137.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.616047919574537
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7651 -0.0042 0.8955 3.8467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95421 0.09130 109.029 < 2e-16 ***
## category_code_LT01_2_count 0.74616 0.08204 9.095 < 2e-16 ***
## category_code_LT01_3_count 0.44538 0.11149 3.995 7.47e-05 ***
## category_code_LT01_5_count 0.94596 0.06245 15.148 < 2e-16 ***
## category_code_LT01_6_count 0.43580 0.15562 2.800 0.0053 **
## category_code_LT01_10_count 0.02951 0.11598 0.254 0.7992
## category_code_LT01_12_count 0.01712 0.20919 0.082 0.9348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.616581698331669
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9938 -0.7611 -0.0184 0.9043 3.8466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95432 0.09124 109.106 < 2e-16 ***
## category_code_LT01_2_count 0.73966 0.08149 9.076 < 2e-16 ***
## category_code_LT01_3_count 0.44232 0.11128 3.975 8.1e-05 ***
## category_code_LT01_5_count 0.94443 0.06222 15.179 < 2e-16 ***
## category_code_LT01_6_count 0.43833 0.15471 2.833 0.0048 **
## category_code_LT01_10_count 0.02702 0.11593 0.233 0.8158
## category_code_LT01_13_count 0.20294 0.24426 0.831 0.4065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.617436654512967
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9984 -0.7650 -0.0212 0.9015 3.8352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.965784 0.091545 108.862 < 2e-16 ***
## category_code_LT01_2_count 0.726121 0.082417 8.810 < 2e-16 ***
## category_code_LT01_3_count 0.450417 0.111118 4.054 5.87e-05 ***
## category_code_LT01_5_count 0.934520 0.062738 14.896 < 2e-16 ***
## category_code_LT01_6_count 0.459052 0.155396 2.954 0.00329 **
## category_code_LT01_10_count -0.007735 0.119085 -0.065 0.94824
## category_code_LT01_14_count 0.450374 0.336711 1.338 0.18166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.616049663480487
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7657 -0.0078 0.8954 3.8470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95398 0.09133 108.995 < 2e-16 ***
## category_code_LT01_2_count 0.74761 0.08116 9.212 < 2e-16 ***
## category_code_LT01_3_count 0.44728 0.11216 3.988 7.68e-05 ***
## category_code_LT01_5_count 0.94626 0.06224 15.204 < 2e-16 ***
## category_code_LT01_6_count 0.43755 0.15488 2.825 0.00492 **
## category_code_LT01_10_count 0.03024 0.11614 0.260 0.79467
## category_code_LT01_15_count -0.07203 0.76245 -0.094 0.92477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.616043634087626
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7659 -0.0059 0.8960 3.8467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95425 0.09132 109.003 < 2e-16 ***
## category_code_LT01_2_count 0.74692 0.08146 9.170 < 2e-16 ***
## category_code_LT01_3_count 0.44560 0.11172 3.989 7.66e-05 ***
## category_code_LT01_5_count 0.94636 0.06223 15.207 < 2e-16 ***
## category_code_LT01_6_count 0.43772 0.15583 2.809 0.00517 **
## category_code_LT01_10_count 0.02950 0.11605 0.254 0.79947
## category_code_LT01_16_count 0.04151 1.19114 0.035 0.97221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.623034198724987
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0091 -0.7878 0.0436 0.8570 3.8288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97220 0.08751 113.949 < 2e-16 ***
## category_code_LT01_2_count 0.61722 0.09184 6.721 5.02e-11 ***
## category_code_LT01_3_count 0.35748 0.11321 3.158 0.00169 **
## category_code_LT01_5_count 0.94062 0.06189 15.198 < 2e-16 ***
## category_code_LT01_6_count 0.38489 0.15385 2.502 0.01269 *
## category_code_LT01_11_count 0.36534 0.12068 3.027 0.00260 **
## category_code_LT01_12_count -0.12897 0.21286 -0.606 0.54487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6276, Adjusted R-squared: 0.623
## F-statistic: 137.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.623069758235952
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0051 -0.7660 0.0542 0.8500 3.8298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97115 0.08751 113.945 < 2e-16 ***
## category_code_LT01_2_count 0.61176 0.09199 6.650 7.82e-11 ***
## category_code_LT01_3_count 0.35573 0.11323 3.142 0.00178 **
## category_code_LT01_5_count 0.93635 0.06174 15.166 < 2e-16 ***
## category_code_LT01_6_count 0.37953 0.15344 2.473 0.01372 *
## category_code_LT01_11_count 0.34350 0.11777 2.917 0.00370 **
## category_code_LT01_13_count 0.15603 0.24266 0.643 0.52052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6276, Adjusted R-squared: 0.6231
## F-statistic: 137.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.623795010719611
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0030 -0.7637 0.0716 0.8534 3.8262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97471 0.08746 114.047 < 2e-16 ***
## category_code_LT01_2_count 0.60081 0.09258 6.489 2.11e-10 ***
## category_code_LT01_3_count 0.35884 0.11310 3.173 0.00160 **
## category_code_LT01_5_count 0.92820 0.06218 14.927 < 2e-16 ***
## category_code_LT01_6_count 0.39240 0.15377 2.552 0.01102 *
## category_code_LT01_11_count 0.33906 0.11767 2.881 0.00413 **
## category_code_LT01_14_count 0.37956 0.32538 1.167 0.24396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6238
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.622772123995862
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0062 -0.7742 0.0514 0.8420 3.8295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97149 0.08754 113.903 < 2e-16 ***
## category_code_LT01_2_count 0.61616 0.09189 6.706 5.53e-11 ***
## category_code_LT01_3_count 0.35971 0.11412 3.152 0.00172 **
## category_code_LT01_5_count 0.93747 0.06175 15.181 < 2e-16 ***
## category_code_LT01_6_count 0.37886 0.15358 2.467 0.01397 *
## category_code_LT01_11_count 0.34918 0.11758 2.970 0.00313 **
## category_code_LT01_15_count -0.12109 0.75490 -0.160 0.87263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6228
## F-statistic: 137.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.622765840862669
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7743 0.0491 0.8415 3.8292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97179 0.08755 113.900 < 2e-16 ***
## category_code_LT01_2_count 0.61438 0.09233 6.655 7.61e-11 ***
## category_code_LT01_3_count 0.35592 0.11383 3.127 0.00187 **
## category_code_LT01_5_count 0.93756 0.06174 15.185 < 2e-16 ***
## category_code_LT01_6_count 0.38015 0.15434 2.463 0.01412 *
## category_code_LT01_11_count 0.34914 0.11759 2.969 0.00313 **
## category_code_LT01_16_count 0.15637 1.18037 0.132 0.89466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6228
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616542241848174
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9989 -0.7548 0.0149 0.8980 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95978 0.08818 112.954 < 2e-16 ***
## category_code_LT01_2_count 0.73944 0.08242 8.971 < 2e-16 ***
## category_code_LT01_3_count 0.44609 0.11004 4.054 5.85e-05 ***
## category_code_LT01_5_count 0.94387 0.06244 15.117 < 2e-16 ***
## category_code_LT01_6_count 0.44239 0.15404 2.872 0.00426 **
## category_code_LT01_12_count 0.01286 0.20912 0.062 0.95098
## category_code_LT01_13_count 0.20406 0.24428 0.835 0.40393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617434341105583
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9971 -0.7646 -0.0200 0.8974 3.8368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.964183 0.088121 113.074 < 2e-16 ***
## category_code_LT01_2_count 0.726600 0.083195 8.734 < 2e-16 ***
## category_code_LT01_3_count 0.449473 0.109813 4.093 4.98e-05 ***
## category_code_LT01_5_count 0.934877 0.062836 14.878 < 2e-16 ***
## category_code_LT01_6_count 0.458042 0.154348 2.968 0.00315 **
## category_code_LT01_12_count -0.007412 0.209624 -0.035 0.97181
## category_code_LT01_14_count 0.446273 0.328607 1.358 0.17506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616002030383174
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0004 -0.7616 0.0228 0.8858 3.8408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96011 0.08824 112.874 < 2e-16 ***
## category_code_LT01_2_count 0.74716 0.08215 9.095 < 2e-16 ***
## category_code_LT01_3_count 0.45117 0.11108 4.062 5.67e-05 ***
## category_code_LT01_5_count 0.94560 0.06246 15.139 < 2e-16 ***
## category_code_LT01_6_count 0.44177 0.15428 2.863 0.00437 **
## category_code_LT01_12_count 0.01737 0.20929 0.083 0.93390
## category_code_LT01_15_count -0.05932 0.76174 -0.078 0.93796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.615998913192535
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0006 -0.7612 0.0226 0.8875 3.8407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96023 0.08824 112.872 < 2e-16 ***
## category_code_LT01_2_count 0.74639 0.08245 9.053 < 2e-16 ***
## category_code_LT01_3_count 0.44949 0.11055 4.066 5.57e-05 ***
## category_code_LT01_5_count 0.94565 0.06246 15.141 < 2e-16 ***
## category_code_LT01_6_count 0.44202 0.15505 2.851 0.00454 **
## category_code_LT01_12_count 0.01802 0.20921 0.086 0.93141
## category_code_LT01_16_count 0.05428 1.19052 0.046 0.96365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.61796369388028
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9952 -0.7606 0.0014 0.9059 3.8372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96376 0.08806 113.147 < 2e-16 ***
## category_code_LT01_2_count 0.71878 0.08284 8.677 < 2e-16 ***
## category_code_LT01_3_count 0.44522 0.10961 4.062 5.66e-05 ***
## category_code_LT01_5_count 0.93285 0.06266 14.886 < 2e-16 ***
## category_code_LT01_6_count 0.45808 0.15329 2.988 0.00294 **
## category_code_LT01_13_count 0.20123 0.24374 0.826 0.40944
## category_code_LT01_14_count 0.44257 0.32710 1.353 0.17667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616539900995703
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9991 -0.7553 0.0144 0.8968 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95976 0.08818 112.948 < 2e-16 ***
## category_code_LT01_2_count 0.74035 0.08162 9.071 < 2e-16 ***
## category_code_LT01_3_count 0.44696 0.11088 4.031 6.44e-05 ***
## category_code_LT01_5_count 0.94416 0.06223 15.172 < 2e-16 ***
## category_code_LT01_6_count 0.44355 0.15330 2.893 0.00398 **
## category_code_LT01_13_count 0.20406 0.24467 0.834 0.40467
## category_code_LT01_15_count -0.02137 0.76235 -0.028 0.97765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.61654480369826
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9991 -0.7551 0.0139 0.8978 3.8411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95988 0.08818 112.947 < 2e-16 ***
## category_code_LT01_2_count 0.73948 0.08193 9.026 < 2e-16 ***
## category_code_LT01_3_count 0.44561 0.11035 4.038 6.25e-05 ***
## category_code_LT01_5_count 0.94409 0.06223 15.171 < 2e-16 ***
## category_code_LT01_6_count 0.44482 0.15413 2.886 0.00407 **
## category_code_LT01_13_count 0.20546 0.24445 0.840 0.40104
## category_code_LT01_16_count 0.10009 1.19087 0.084 0.93306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.617443189413316
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9967 -0.7607 -0.0080 0.8962 3.8369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96407 0.08812 113.069 < 2e-16 ***
## category_code_LT01_2_count 0.72665 0.08251 8.807 < 2e-16 ***
## category_code_LT01_3_count 0.45091 0.11060 4.077 5.32e-05 ***
## category_code_LT01_5_count 0.93452 0.06269 14.907 < 2e-16 ***
## category_code_LT01_6_count 0.45813 0.15351 2.984 0.00298 **
## category_code_LT01_14_count 0.44610 0.32739 1.363 0.17364
## category_code_LT01_15_count -0.08535 0.76015 -0.112 0.91065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617447931783118
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9970 -0.7635 -0.0187 0.9018 3.8366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96435 0.08813 113.066 < 2e-16 ***
## category_code_LT01_2_count 0.72492 0.08294 8.740 < 2e-16 ***
## category_code_LT01_3_count 0.44775 0.11010 4.067 5.56e-05 ***
## category_code_LT01_5_count 0.93447 0.06269 14.906 < 2e-16 ***
## category_code_LT01_6_count 0.45986 0.15441 2.978 0.00304 **
## category_code_LT01_14_count 0.44828 0.32805 1.366 0.17242
## category_code_LT01_16_count 0.16282 1.19086 0.137 0.89130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.615997935337491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0007 -0.7631 0.0223 0.8876 3.8408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96016 0.08825 112.864 < 2e-16 ***
## category_code_LT01_2_count 0.74788 0.08157 9.169 < 2e-16 ***
## category_code_LT01_3_count 0.45135 0.11139 4.052 5.91e-05 ***
## category_code_LT01_5_count 0.94599 0.06225 15.198 < 2e-16 ***
## category_code_LT01_6_count 0.44380 0.15432 2.876 0.0042 **
## category_code_LT01_15_count -0.05985 0.76220 -0.079 0.9374
## category_code_LT01_16_count 0.04841 1.19167 0.041 0.9676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.622512626510026
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0161 -0.7601 0.0005 0.8298 3.8274
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97352 0.08741 114.102 < 2e-16 ***
## category_code_LT01_2_count 0.72638 0.07923 9.168 < 2e-16 ***
## category_code_LT01_3_count 0.42931 0.10967 3.914 0.000103 ***
## category_code_LT01_5_count 0.94892 0.06223 15.249 < 2e-16 ***
## category_code_LT01_7_count 0.57454 0.15149 3.793 0.000168 ***
## category_code_LT01_8_count -0.15620 0.27420 -0.570 0.569175
## category_code_LT01_9_count 0.27232 0.22861 1.191 0.234147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6271, Adjusted R-squared: 0.6225
## F-statistic: 137.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.621542524486735
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0121 -0.7224 -0.0219 0.8336 3.8346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96633 0.09067 109.915 < 2e-16 ***
## category_code_LT01_2_count 0.73585 0.07891 9.326 < 2e-16 ***
## category_code_LT01_3_count 0.44033 0.11036 3.990 7.62e-05 ***
## category_code_LT01_5_count 0.95347 0.06220 15.330 < 2e-16 ***
## category_code_LT01_7_count 0.58737 0.15138 3.880 0.000119 ***
## category_code_LT01_8_count -0.14854 0.27448 -0.541 0.588642
## category_code_LT01_10_count 0.04526 0.11431 0.396 0.692332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6215
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.625695907807312
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0242 -0.7373 0.0109 0.8068 3.8177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98324 0.08708 114.647 < 2e-16 ***
## category_code_LT01_2_count 0.63570 0.08941 7.110 4.11e-12 ***
## category_code_LT01_3_count 0.37438 0.11247 3.329 0.000938 ***
## category_code_LT01_5_count 0.94656 0.06192 15.286 < 2e-16 ***
## category_code_LT01_7_count 0.49036 0.15614 3.140 0.001789 **
## category_code_LT01_8_count -0.12717 0.27304 -0.466 0.641592
## category_code_LT01_11_count 0.28539 0.12053 2.368 0.018278 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.621526528854333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0198 -0.7331 -0.0047 0.8255 3.8254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97557 0.08750 114.001 < 2e-16 ***
## category_code_LT01_2_count 0.73211 0.08026 9.122 < 2e-16 ***
## category_code_LT01_3_count 0.44476 0.10903 4.079 5.27e-05 ***
## category_code_LT01_5_count 0.95123 0.06246 15.230 < 2e-16 ***
## category_code_LT01_7_count 0.59106 0.15102 3.914 0.000104 ***
## category_code_LT01_8_count -0.15038 0.27463 -0.548 0.584237
## category_code_LT01_12_count 0.07622 0.20669 0.369 0.712450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6215
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.621484452580535
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0208 -0.7579 -0.0001 0.8314 3.8254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97556 0.08751 113.993 < 2e-16 ***
## category_code_LT01_2_count 0.73618 0.07896 9.323 < 2e-16 ***
## category_code_LT01_3_count 0.44692 0.10876 4.109 4.65e-05 ***
## category_code_LT01_5_count 0.95270 0.06224 15.307 < 2e-16 ***
## category_code_LT01_7_count 0.58579 0.15238 3.844 0.000137 ***
## category_code_LT01_8_count -0.14178 0.27497 -0.516 0.606350
## category_code_LT01_13_count 0.06995 0.24517 0.285 0.775524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6215
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.621904400958989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0201 -0.7355 0.0024 0.8097 3.8226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97837 0.08752 114.009 < 2e-16 ***
## category_code_LT01_2_count 0.72876 0.07954 9.162 < 2e-16 ***
## category_code_LT01_3_count 0.44887 0.10865 4.131 4.24e-05 ***
## category_code_LT01_5_count 0.94764 0.06258 15.142 < 2e-16 ***
## category_code_LT01_7_count 0.58001 0.15164 3.825 0.000148 ***
## category_code_LT01_8_count -0.14983 0.27433 -0.546 0.585191
## category_code_LT01_14_count 0.25822 0.32615 0.792 0.428897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6265, Adjusted R-squared: 0.6219
## F-statistic: 137.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.621438033022439
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0216 -0.7354 -0.0018 0.8310 3.8251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97585 0.08752 113.987 < 2e-16 ***
## category_code_LT01_2_count 0.73692 0.07900 9.328 < 2e-16 ***
## category_code_LT01_3_count 0.44558 0.10986 4.056 5.81e-05 ***
## category_code_LT01_5_count 0.95350 0.06222 15.325 < 2e-16 ***
## category_code_LT01_7_count 0.59221 0.15109 3.920 0.000101 ***
## category_code_LT01_8_count -0.14701 0.27448 -0.536 0.592479
## category_code_LT01_15_count 0.11000 0.75572 0.146 0.884329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6214
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.621434206689943
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0212 -0.7367 -0.0023 0.8297 3.8254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97551 0.08753 113.964 < 2e-16 ***
## category_code_LT01_2_count 0.73856 0.07897 9.352 < 2e-16 ***
## category_code_LT01_3_count 0.44905 0.10910 4.116 4.52e-05 ***
## category_code_LT01_5_count 0.95333 0.06221 15.325 < 2e-16 ***
## category_code_LT01_7_count 0.59095 0.15111 3.911 0.000105 ***
## category_code_LT01_8_count -0.14488 0.27480 -0.527 0.598290
## category_code_LT01_16_count -0.14989 1.17674 -0.127 0.898696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6214
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.62232358557882
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0044 -0.7507 -0.0192 0.8318 3.8356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96534 0.09055 110.054 < 2e-16 ***
## category_code_LT01_2_count 0.72595 0.07935 9.149 < 2e-16 ***
## category_code_LT01_3_count 0.42295 0.11110 3.807 0.000158 ***
## category_code_LT01_5_count 0.94384 0.06156 15.331 < 2e-16 ***
## category_code_LT01_7_count 0.56911 0.15174 3.750 0.000198 ***
## category_code_LT01_9_count 0.26260 0.22953 1.144 0.253154
## category_code_LT01_10_count 0.03214 0.11465 0.280 0.779354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6269, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.626545698312474
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7693 0.0324 0.8317 3.8212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97974 0.08696 114.761 < 2e-16 ***
## category_code_LT01_2_count 0.62509 0.08976 6.964 1.07e-11 ***
## category_code_LT01_3_count 0.35499 0.11331 3.133 0.00183 **
## category_code_LT01_5_count 0.93767 0.06127 15.304 < 2e-16 ***
## category_code_LT01_7_count 0.47122 0.15645 3.012 0.00273 **
## category_code_LT01_9_count 0.26260 0.22730 1.155 0.24853
## category_code_LT01_11_count 0.28556 0.12034 2.373 0.01803 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6265
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.622358177434186
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0093 -0.7612 -0.0033 0.8355 3.8292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97179 0.08739 114.110 < 2e-16 ***
## category_code_LT01_2_count 0.72167 0.08073 8.939 < 2e-16 ***
## category_code_LT01_3_count 0.42491 0.11000 3.863 0.000127 ***
## category_code_LT01_5_count 0.94156 0.06185 15.222 < 2e-16 ***
## category_code_LT01_7_count 0.57118 0.15145 3.771 0.000182 ***
## category_code_LT01_9_count 0.26867 0.22856 1.175 0.240368
## category_code_LT01_12_count 0.07252 0.20631 0.352 0.725357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6269, Adjusted R-squared: 0.6224
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.622391544013218
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0102 -0.7601 0.0005 0.8339 3.8292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97174 0.08738 114.115 < 2e-16 ***
## category_code_LT01_2_count 0.72443 0.07950 9.112 < 2e-16 ***
## category_code_LT01_3_count 0.42613 0.10975 3.883 0.000117 ***
## category_code_LT01_5_count 0.94290 0.06159 15.310 < 2e-16 ***
## category_code_LT01_7_count 0.56314 0.15290 3.683 0.000256 ***
## category_code_LT01_9_count 0.27563 0.22921 1.202 0.229753
## category_code_LT01_13_count 0.10016 0.24513 0.409 0.683010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.627, Adjusted R-squared: 0.6224
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.622649061263673
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0099 -0.7575 0.0149 0.8303 3.8266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97440 0.08742 114.100 < 2e-16 ***
## category_code_LT01_2_count 0.71944 0.07994 8.999 < 2e-16 ***
## category_code_LT01_3_count 0.42952 0.10965 3.917 0.000102 ***
## category_code_LT01_5_count 0.93867 0.06194 15.154 < 2e-16 ***
## category_code_LT01_7_count 0.56201 0.15201 3.697 0.000243 ***
## category_code_LT01_9_count 0.25781 0.22896 1.126 0.260723
## category_code_LT01_14_count 0.23136 0.32650 0.709 0.478895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6272, Adjusted R-squared: 0.6226
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.622289136800552
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0112 -0.7580 -0.0091 0.8328 3.8288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97211 0.08740 114.102 < 2e-16 ***
## category_code_LT01_2_count 0.72590 0.07952 9.129 < 2e-16 ***
## category_code_LT01_3_count 0.42491 0.11090 3.831 0.000144 ***
## category_code_LT01_5_count 0.94386 0.06157 15.329 < 2e-16 ***
## category_code_LT01_7_count 0.57243 0.15150 3.778 0.000177 ***
## category_code_LT01_9_count 0.27005 0.22873 1.181 0.238320
## category_code_LT01_15_count 0.13884 0.75535 0.184 0.854235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.622286717350137
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0107 -0.7581 -0.0155 0.8333 3.8293
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97169 0.08741 114.080 < 2e-16 ***
## category_code_LT01_2_count 0.72807 0.07944 9.165 < 2e-16 ***
## category_code_LT01_3_count 0.42951 0.11005 3.903 0.000108 ***
## category_code_LT01_5_count 0.94376 0.06157 15.329 < 2e-16 ***
## category_code_LT01_7_count 0.57089 0.15154 3.767 0.000185 ***
## category_code_LT01_9_count 0.26922 0.22862 1.178 0.239521
## category_code_LT01_16_count -0.20554 1.17420 -0.175 0.861113
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.625672763264035
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0098 -0.7598 0.0344 0.8273 3.8292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97180 0.09019 110.563 < 2e-16 ***
## category_code_LT01_2_count 0.63308 0.08959 7.066 5.48e-12 ***
## category_code_LT01_3_count 0.36414 0.11410 3.191 0.00151 **
## category_code_LT01_5_count 0.94227 0.06122 15.393 < 2e-16 ***
## category_code_LT01_7_count 0.48235 0.15643 3.083 0.00216 **
## category_code_LT01_10_count 0.04910 0.11368 0.432 0.66598
## category_code_LT01_11_count 0.28803 0.12050 2.390 0.01721 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.621404781345681
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7437 -0.0164 0.8400 3.8359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96510 0.09066 109.918 < 2e-16 ***
## category_code_LT01_2_count 0.73126 0.08039 9.097 < 2e-16 ***
## category_code_LT01_3_count 0.43623 0.11064 3.943 9.23e-05 ***
## category_code_LT01_5_count 0.94639 0.06181 15.312 < 2e-16 ***
## category_code_LT01_7_count 0.58415 0.15133 3.860 0.000128 ***
## category_code_LT01_10_count 0.04307 0.11436 0.377 0.706621
## category_code_LT01_12_count 0.06981 0.20666 0.338 0.735657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6214
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.621391339206155
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0070 -0.7546 -0.0204 0.8435 3.8359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96506 0.09066 109.915 < 2e-16 ***
## category_code_LT01_2_count 0.73466 0.07912 9.285 < 2e-16 ***
## category_code_LT01_3_count 0.43806 0.11039 3.968 8.32e-05 ***
## category_code_LT01_5_count 0.94793 0.06153 15.406 < 2e-16 ***
## category_code_LT01_7_count 0.57846 0.15261 3.790 0.000169 ***
## category_code_LT01_10_count 0.04354 0.11433 0.381 0.703479
## category_code_LT01_13_count 0.07611 0.24478 0.311 0.755991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.626, Adjusted R-squared: 0.6214
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.621712658093617
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0098 -0.7574 -0.0106 0.8264 3.8297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97126 0.09103 109.534 < 2e-16 ***
## category_code_LT01_2_count 0.72882 0.07959 9.157 < 2e-16 ***
## category_code_LT01_3_count 0.44288 0.11044 4.010 7.02e-05 ***
## category_code_LT01_5_count 0.94302 0.06195 15.223 < 2e-16 ***
## category_code_LT01_7_count 0.57551 0.15179 3.791 0.000168 ***
## category_code_LT01_10_count 0.02598 0.11704 0.222 0.824401
## category_code_LT01_14_count 0.23950 0.33412 0.717 0.473834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.621327258901065
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0077 -0.7460 -0.0263 0.8362 3.8356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96532 0.09070 109.877 < 2e-16 ***
## category_code_LT01_2_count 0.73575 0.07914 9.297 < 2e-16 ***
## category_code_LT01_3_count 0.43724 0.11134 3.927 9.83e-05 ***
## category_code_LT01_5_count 0.94856 0.06152 15.418 < 2e-16 ***
## category_code_LT01_7_count 0.58517 0.15142 3.865 0.000126 ***
## category_code_LT01_10_count 0.04333 0.11454 0.378 0.705335
## category_code_LT01_15_count 0.08820 0.75721 0.116 0.907317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6213
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.621337149279853
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0070 -0.7454 -0.0284 0.8378 3.8362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96471 0.09069 109.872 < 2e-16 ***
## category_code_LT01_2_count 0.73734 0.07912 9.319 < 2e-16 ***
## category_code_LT01_3_count 0.44039 0.11072 3.978 8.01e-05 ***
## category_code_LT01_5_count 0.94851 0.06152 15.419 < 2e-16 ***
## category_code_LT01_7_count 0.58379 0.15142 3.855 0.000131 ***
## category_code_LT01_10_count 0.04459 0.11435 0.390 0.696780
## category_code_LT01_16_count -0.19102 1.17580 -0.162 0.871010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6213
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.625575234375501
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0212 -0.7695 0.0182 0.8142 3.8186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98234 0.08706 114.657 < 2e-16 ***
## category_code_LT01_2_count 0.63674 0.08956 7.110 4.12e-12 ***
## category_code_LT01_3_count 0.37284 0.11243 3.316 0.00098 ***
## category_code_LT01_5_count 0.94355 0.06147 15.349 < 2e-16 ***
## category_code_LT01_7_count 0.48509 0.15630 3.104 0.00202 **
## category_code_LT01_11_count 0.29447 0.12430 2.369 0.01822 *
## category_code_LT01_12_count -0.05131 0.21192 -0.242 0.80879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.625575004148832
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.7655 0.0241 0.8176 3.8191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98188 0.08705 114.664 < 2e-16 ***
## category_code_LT01_2_count 0.63450 0.08953 7.087 4.78e-12 ***
## category_code_LT01_3_count 0.37208 0.11244 3.309 0.00100 **
## category_code_LT01_5_count 0.94184 0.06124 15.379 < 2e-16 ***
## category_code_LT01_7_count 0.48284 0.15712 3.073 0.00224 **
## category_code_LT01_11_count 0.28613 0.12056 2.373 0.01801 *
## category_code_LT01_13_count 0.05881 0.24351 0.241 0.80927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.625911394195205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0187 -0.7629 0.0238 0.8184 3.8167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98424 0.08707 114.670 < 2e-16 ***
## category_code_LT01_2_count 0.62854 0.08993 6.989 9.05e-12 ***
## category_code_LT01_3_count 0.37420 0.11239 3.329 0.000936 ***
## category_code_LT01_5_count 0.93711 0.06162 15.209 < 2e-16 ***
## category_code_LT01_7_count 0.47798 0.15652 3.054 0.002382 **
## category_code_LT01_11_count 0.28417 0.12051 2.358 0.018758 *
## category_code_LT01_14_count 0.22948 0.32457 0.707 0.479884
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.625531941084013
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0200 -0.7698 0.0176 0.8168 3.8189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98203 0.08706 114.658 < 2e-16 ***
## category_code_LT01_2_count 0.63536 0.08953 7.096 4.5e-12 ***
## category_code_LT01_3_count 0.37198 0.11336 3.281 0.00111 **
## category_code_LT01_5_count 0.94226 0.06124 15.387 < 2e-16 ***
## category_code_LT01_7_count 0.48755 0.15615 3.122 0.00190 **
## category_code_LT01_11_count 0.28686 0.12060 2.379 0.01776 *
## category_code_LT01_15_count 0.03234 0.75222 0.043 0.96573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.625533725657148
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0199 -0.7694 0.0212 0.8169 3.8191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98189 0.08707 114.637 < 2e-16 ***
## category_code_LT01_2_count 0.63603 0.08972 7.089 4.74e-12 ***
## category_code_LT01_3_count 0.37328 0.11291 3.306 0.00102 **
## category_code_LT01_5_count 0.94225 0.06123 15.389 < 2e-16 ***
## category_code_LT01_7_count 0.48710 0.15607 3.121 0.00191 **
## category_code_LT01_11_count 0.28678 0.12058 2.378 0.01777 *
## category_code_LT01_16_count -0.07570 1.16977 -0.065 0.94843
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.621368693847484
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0144 -0.7635 -0.0043 0.8315 3.8270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97394 0.08748 114.013 < 2e-16 ***
## category_code_LT01_2_count 0.73131 0.08044 9.091 < 2e-16 ***
## category_code_LT01_3_count 0.44244 0.10906 4.057 5.79e-05 ***
## category_code_LT01_5_count 0.94580 0.06182 15.298 < 2e-16 ***
## category_code_LT01_7_count 0.58205 0.15226 3.823 0.000149 ***
## category_code_LT01_12_count 0.07029 0.20666 0.340 0.733921
## category_code_LT01_13_count 0.07547 0.24483 0.308 0.758009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6214
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.621738089386113
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0138 -0.7520 0.0068 0.8266 3.8244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97660 0.08750 114.017 < 2e-16 ***
## category_code_LT01_2_count 0.72521 0.08087 8.967 < 2e-16 ***
## category_code_LT01_3_count 0.44479 0.10899 4.081 5.23e-05 ***
## category_code_LT01_5_count 0.94104 0.06216 15.140 < 2e-16 ***
## category_code_LT01_7_count 0.57710 0.15159 3.807 0.000158 ***
## category_code_LT01_12_count 0.05943 0.20715 0.287 0.774324
## category_code_LT01_14_count 0.24805 0.32723 0.758 0.448795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.621312572487
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7536 -0.0099 0.8255 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97418 0.08749 114.004 < 2e-16 ***
## category_code_LT01_2_count 0.73198 0.08054 9.089 < 2e-16 ***
## category_code_LT01_3_count 0.44095 0.11021 4.001 7.28e-05 ***
## category_code_LT01_5_count 0.94639 0.06182 15.309 < 2e-16 ***
## category_code_LT01_7_count 0.58880 0.15102 3.899 0.00011 ***
## category_code_LT01_12_count 0.07281 0.20667 0.352 0.72478
## category_code_LT01_15_count 0.11278 0.75607 0.149 0.88149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6213
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.621311490217618
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0147 -0.7579 -0.0120 0.8302 3.8271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97384 0.08750 113.985 < 2e-16 ***
## category_code_LT01_2_count 0.73385 0.08050 9.116 < 2e-16 ***
## category_code_LT01_3_count 0.44473 0.10945 4.063 5.63e-05 ***
## category_code_LT01_5_count 0.94634 0.06182 15.309 < 2e-16 ***
## category_code_LT01_7_count 0.58751 0.15104 3.890 0.000114 ***
## category_code_LT01_12_count 0.07122 0.20667 0.345 0.730550
## category_code_LT01_16_count -0.16978 1.17591 -0.144 0.885259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6213
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.621753358676536
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7583 0.0055 0.8237 3.8243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97670 0.08750 114.024 < 2e-16 ***
## category_code_LT01_2_count 0.72753 0.07976 9.121 < 2e-16 ***
## category_code_LT01_3_count 0.44627 0.10868 4.106 4.71e-05 ***
## category_code_LT01_5_count 0.94209 0.06194 15.211 < 2e-16 ***
## category_code_LT01_7_count 0.57085 0.15290 3.734 0.000211 ***
## category_code_LT01_13_count 0.07817 0.24462 0.320 0.749429
## category_code_LT01_14_count 0.25582 0.32618 0.784 0.433244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6218
## F-statistic: 137.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.621299032912271
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0162 -0.7555 -0.0128 0.8285 3.8267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97424 0.08749 114.004 < 2e-16 ***
## category_code_LT01_2_count 0.73547 0.07925 9.280 < 2e-16 ***
## category_code_LT01_3_count 0.44277 0.10992 4.028 6.52e-05 ***
## category_code_LT01_5_count 0.94800 0.06155 15.403 < 2e-16 ***
## category_code_LT01_7_count 0.58289 0.15230 3.827 0.000146 ***
## category_code_LT01_13_count 0.08004 0.24521 0.326 0.744256
## category_code_LT01_15_count 0.12052 0.75718 0.159 0.873604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6213
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.621294600284244
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0158 -0.7635 -0.0160 0.8380 3.8271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97390 0.08750 113.984 < 2e-16 ***
## category_code_LT01_2_count 0.73735 0.07921 9.309 < 2e-16 ***
## category_code_LT01_3_count 0.44666 0.10915 4.092 5e-05 ***
## category_code_LT01_5_count 0.94792 0.06154 15.403 < 2e-16 ***
## category_code_LT01_7_count 0.58188 0.15233 3.820 0.000151 ***
## category_code_LT01_13_count 0.07624 0.24499 0.311 0.755789
## category_code_LT01_16_count -0.16467 1.17661 -0.140 0.888756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6259, Adjusted R-squared: 0.6213
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.621686117025381
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0153 -0.7463 -0.0003 0.8216 3.8240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97692 0.08751 114.015 < 2e-16 ***
## category_code_LT01_2_count 0.72866 0.07978 9.134 < 2e-16 ***
## category_code_LT01_3_count 0.44535 0.10980 4.056 5.81e-05 ***
## category_code_LT01_5_count 0.94277 0.06193 15.222 < 2e-16 ***
## category_code_LT01_7_count 0.57778 0.15167 3.810 0.000157 ***
## category_code_LT01_14_count 0.25464 0.32629 0.780 0.435528
## category_code_LT01_15_count 0.09204 0.75564 0.122 0.903102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.621683897833116
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0150 -0.7477 -0.0026 0.8215 3.8243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97664 0.08752 113.990 < 2e-16 ***
## category_code_LT01_2_count 0.73008 0.07981 9.148 < 2e-16 ***
## category_code_LT01_3_count 0.44830 0.10904 4.111 4.61e-05 ***
## category_code_LT01_5_count 0.94271 0.06193 15.223 < 2e-16 ***
## category_code_LT01_7_count 0.57681 0.15165 3.804 0.000161 ***
## category_code_LT01_14_count 0.25354 0.32673 0.776 0.438117
## category_code_LT01_16_count -0.12868 1.17682 -0.109 0.912972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.621233382905003
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0165 -0.7575 -0.0179 0.8284 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97413 0.08751 113.973 < 2e-16 ***
## category_code_LT01_2_count 0.73837 0.07925 9.317 < 2e-16 ***
## category_code_LT01_3_count 0.44561 0.11032 4.039 6.22e-05 ***
## category_code_LT01_5_count 0.94858 0.06153 15.416 < 2e-16 ***
## category_code_LT01_7_count 0.58861 0.15111 3.895 0.000112 ***
## category_code_LT01_15_count 0.10003 0.75688 0.132 0.894913
## category_code_LT01_16_count -0.17229 1.17719 -0.146 0.883701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6258, Adjusted R-squared: 0.6212
## F-statistic: 136.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.611664462399198
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0151 -0.7500 0.0324 0.8660 3.8405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96041 0.09183 108.461 < 2e-16 ***
## category_code_LT01_2_count 0.81145 0.07705 10.532 < 2e-16 ***
## category_code_LT01_3_count 0.45910 0.11229 4.089 5.07e-05 ***
## category_code_LT01_5_count 0.96628 0.06295 15.350 < 2e-16 ***
## category_code_LT01_8_count -0.12519 0.27801 -0.450 0.653
## category_code_LT01_9_count 0.34263 0.23193 1.477 0.140
## category_code_LT01_10_count 0.05982 0.11605 0.515 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6164, Adjusted R-squared: 0.6117
## F-statistic: 131.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.61975686740347
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0286 -0.7714 0.0161 0.8120 4.1172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98307 0.08778 113.723 < 2e-16 ***
## category_code_LT01_2_count 0.65700 0.08995 7.304 1.13e-12 ***
## category_code_LT01_3_count 0.36139 0.11437 3.160 0.00168 **
## category_code_LT01_5_count 0.95308 0.06242 15.270 < 2e-16 ***
## category_code_LT01_8_count -0.10425 0.27511 -0.379 0.70490
## category_code_LT01_9_count 0.32641 0.22857 1.428 0.15392
## category_code_LT01_11_count 0.38282 0.11692 3.274 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6198
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.611583747090833
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.7621 0.0313 0.8723 3.8284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97257 0.08867 112.473 < 2e-16 ***
## category_code_LT01_2_count 0.80789 0.07849 10.293 < 2e-16 ***
## category_code_LT01_3_count 0.46514 0.11109 4.187 3.35e-05 ***
## category_code_LT01_5_count 0.96370 0.06322 15.242 < 2e-16 ***
## category_code_LT01_8_count -0.12697 0.27819 -0.456 0.648
## category_code_LT01_9_count 0.35447 0.23086 1.535 0.125
## category_code_LT01_12_count 0.08469 0.20937 0.404 0.686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6163, Adjusted R-squared: 0.6116
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.612080761833396
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0247 -0.7617 0.0280 0.8977 3.8288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97211 0.08861 112.537 < 2e-16 ***
## category_code_LT01_2_count 0.80563 0.07742 10.406 < 2e-16 ***
## category_code_LT01_3_count 0.46330 0.11084 4.180 3.45e-05 ***
## category_code_LT01_5_count 0.96327 0.06299 15.292 < 2e-16 ***
## category_code_LT01_8_count -0.10928 0.27823 -0.393 0.695
## category_code_LT01_9_count 0.36679 0.23114 1.587 0.113
## category_code_LT01_13_count 0.21944 0.24644 0.890 0.374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.612309677048717
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7660 0.0418 0.8609 3.8246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97639 0.08865 112.535 < 2e-16 ***
## category_code_LT01_2_count 0.80075 0.07788 10.282 < 2e-16 ***
## category_code_LT01_3_count 0.47008 0.11065 4.248 2.58e-05 ***
## category_code_LT01_5_count 0.95832 0.06333 15.131 < 2e-16 ***
## category_code_LT01_8_count -0.12731 0.27777 -0.458 0.647
## category_code_LT01_9_count 0.33645 0.23127 1.455 0.146
## category_code_LT01_14_count 0.34307 0.32962 1.041 0.298
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.617, Adjusted R-squared: 0.6123
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.611461752641505
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0274 -0.7657 0.0253 0.8794 3.8281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97282 0.08868 112.456 < 2e-16 ***
## category_code_LT01_2_count 0.81375 0.07710 10.554 < 2e-16 ***
## category_code_LT01_3_count 0.46709 0.11197 4.171 3.58e-05 ***
## category_code_LT01_5_count 0.96617 0.06298 15.342 < 2e-16 ***
## category_code_LT01_8_count -0.12301 0.27805 -0.442 0.658
## category_code_LT01_9_count 0.35514 0.23108 1.537 0.125
## category_code_LT01_15_count 0.07425 0.76593 0.097 0.923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6115
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.611514054075749
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0267 -0.7631 0.0260 0.8706 3.8287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97225 0.08869 112.437 < 2e-16 ***
## category_code_LT01_2_count 0.81568 0.07701 10.592 < 2e-16 ***
## category_code_LT01_3_count 0.47106 0.11110 4.240 2.67e-05 ***
## category_code_LT01_5_count 0.96601 0.06296 15.343 < 2e-16 ***
## category_code_LT01_8_count -0.11909 0.27833 -0.428 0.669
## category_code_LT01_9_count 0.35511 0.23090 1.538 0.125
## category_code_LT01_16_count -0.32740 1.19151 -0.275 0.784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6115
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.618516428952513
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.7597 0.0042 0.8353 4.0993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97016 0.09107 109.475 < 2e-16 ***
## category_code_LT01_2_count 0.66707 0.08977 7.431 4.82e-13 ***
## category_code_LT01_3_count 0.37036 0.11523 3.214 0.001395 **
## category_code_LT01_5_count 0.95883 0.06239 15.369 < 2e-16 ***
## category_code_LT01_8_count -0.09486 0.27546 -0.344 0.730732
## category_code_LT01_10_count 0.07560 0.11448 0.660 0.509322
## category_code_LT01_11_count 0.38885 0.11703 3.323 0.000958 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6231, Adjusted R-squared: 0.6185
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.61005404553441
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0173 -0.7530 0.0156 0.8401 3.8411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95981 0.09202 108.232 < 2e-16 ***
## category_code_LT01_2_count 0.82229 0.07797 10.547 < 2e-16 ***
## category_code_LT01_3_count 0.47815 0.11181 4.276 2.28e-05 ***
## category_code_LT01_5_count 0.97030 0.06321 15.351 < 2e-16 ***
## category_code_LT01_8_count -0.11650 0.27864 -0.418 0.676
## category_code_LT01_10_count 0.07523 0.11579 0.650 0.516
## category_code_LT01_12_count 0.08011 0.20987 0.382 0.703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.610415695707282
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0172 -0.7605 0.0168 0.8433 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95977 0.09198 108.282 < 2e-16 ***
## category_code_LT01_2_count 0.82143 0.07683 10.691 < 2e-16 ***
## category_code_LT01_3_count 0.47780 0.11155 4.283 2.21e-05 ***
## category_code_LT01_5_count 0.97030 0.06297 15.408 < 2e-16 ***
## category_code_LT01_8_count -0.10028 0.27877 -0.360 0.719
## category_code_LT01_10_count 0.07401 0.11573 0.639 0.523
## category_code_LT01_13_count 0.19128 0.24660 0.776 0.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.610776523485523
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0222 -0.7584 0.0311 0.8487 3.8321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96889 0.09237 107.926 < 2e-16 ***
## category_code_LT01_2_count 0.81520 0.07734 10.540 < 2e-16 ***
## category_code_LT01_3_count 0.48607 0.11150 4.359 1.59e-05 ***
## category_code_LT01_5_count 0.96436 0.06338 15.216 < 2e-16 ***
## category_code_LT01_8_count -0.11658 0.27822 -0.419 0.675
## category_code_LT01_10_count 0.04945 0.11858 0.417 0.677
## category_code_LT01_14_count 0.34732 0.33776 1.028 0.304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.609938336555081
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0187 -0.7557 0.0213 0.8354 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.959725 0.092063 108.184 < 2e-16 ***
## category_code_LT01_2_count 0.828331 0.076544 10.822 < 2e-16 ***
## category_code_LT01_3_count 0.481308 0.112474 4.279 2.25e-05 ***
## category_code_LT01_5_count 0.972519 0.062963 15.446 < 2e-16 ***
## category_code_LT01_8_count -0.112535 0.278502 -0.404 0.686
## category_code_LT01_10_count 0.076493 0.115960 0.660 0.510
## category_code_LT01_15_count -0.002564 0.768147 -0.003 0.997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6099
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.609994831719513
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0181 -0.7487 0.0125 0.8359 3.8418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95912 0.09206 108.183 < 2e-16 ***
## category_code_LT01_2_count 0.82962 0.07652 10.842 < 2e-16 ***
## category_code_LT01_3_count 0.48353 0.11184 4.323 1.86e-05 ***
## category_code_LT01_5_count 0.97249 0.06294 15.450 < 2e-16 ***
## category_code_LT01_8_count -0.10902 0.27879 -0.391 0.696
## category_code_LT01_10_count 0.07709 0.11577 0.666 0.506
## category_code_LT01_16_count -0.31845 1.19397 -0.267 0.790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.61
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.618305517017228
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0372 -0.7590 -0.0109 0.8140 4.0557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98638 0.08794 113.562 < 2e-16 ***
## category_code_LT01_2_count 0.67315 0.08965 7.509 2.84e-13 ***
## category_code_LT01_3_count 0.38374 0.11354 3.380 0.000783 ***
## category_code_LT01_5_count 0.96080 0.06260 15.349 < 2e-16 ***
## category_code_LT01_8_count -0.08627 0.27575 -0.313 0.754529
## category_code_LT01_11_count 0.40078 0.12059 3.324 0.000955 ***
## category_code_LT01_12_count -0.08673 0.21381 -0.406 0.685183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.6183
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.618442408399854
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0338 -0.7721 -0.0043 0.8272 4.0693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98541 0.08792 113.578 < 2e-16 ***
## category_code_LT01_2_count 0.66805 0.08972 7.446 4.36e-13 ***
## category_code_LT01_3_count 0.38189 0.11355 3.363 0.000831 ***
## category_code_LT01_5_count 0.95731 0.06245 15.330 < 2e-16 ***
## category_code_LT01_8_count -0.08230 0.27585 -0.298 0.765575
## category_code_LT01_11_count 0.38451 0.11729 3.278 0.001119 **
## category_code_LT01_13_count 0.14272 0.24449 0.584 0.559659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.618900875616077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0334 -0.7641 0.0064 0.8209 4.0768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98892 0.08792 113.618 < 2e-16 ***
## category_code_LT01_2_count 0.66088 0.09015 7.331 9.46e-13 ***
## category_code_LT01_3_count 0.38551 0.11347 3.398 0.000735 ***
## category_code_LT01_5_count 0.95164 0.06280 15.154 < 2e-16 ***
## category_code_LT01_8_count -0.09632 0.27532 -0.350 0.726617
## category_code_LT01_11_count 0.38230 0.11718 3.263 0.001181 **
## category_code_LT01_14_count 0.31517 0.32649 0.965 0.334858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.618181502448595
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0352 -0.7682 -0.0035 0.8210 4.0584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98583 0.08795 113.545 < 2e-16 ***
## category_code_LT01_2_count 0.67171 0.08965 7.492 3.18e-13 ***
## category_code_LT01_3_count 0.38452 0.11448 3.359 0.000843 ***
## category_code_LT01_5_count 0.95870 0.06243 15.356 < 2e-16 ***
## category_code_LT01_8_count -0.09095 0.27554 -0.330 0.741487
## category_code_LT01_11_count 0.38930 0.11714 3.323 0.000956 ***
## category_code_LT01_15_count -0.05375 0.75906 -0.071 0.943576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.618187084364546
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0352 -0.7681 -0.0068 0.8197 4.0574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98567 0.08796 113.521 < 2e-16 ***
## category_code_LT01_2_count 0.67221 0.08985 7.482 3.41e-13 ***
## category_code_LT01_3_count 0.38463 0.11402 3.373 0.000801 ***
## category_code_LT01_5_count 0.95881 0.06242 15.362 < 2e-16 ***
## category_code_LT01_8_count -0.08972 0.27583 -0.325 0.745124
## category_code_LT01_11_count 0.38846 0.11719 3.315 0.000985 ***
## category_code_LT01_16_count -0.13055 1.18226 -0.110 0.912121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.610203694173807
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0309 -0.7645 0.0095 0.8793 3.8260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97498 0.08881 112.323 < 2e-16 ***
## category_code_LT01_2_count 0.81970 0.07817 10.486 < 2e-16 ***
## category_code_LT01_3_count 0.48739 0.11010 4.427 1.18e-05 ***
## category_code_LT01_5_count 0.96807 0.06325 15.305 < 2e-16 ***
## category_code_LT01_8_count -0.10052 0.27900 -0.360 0.719
## category_code_LT01_12_count 0.07899 0.20984 0.376 0.707
## category_code_LT01_13_count 0.19280 0.24669 0.782 0.435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6149, Adjusted R-squared: 0.6102
## F-statistic: 130.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.610714549479067
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7688 0.0154 0.8377 3.8217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97926 0.08881 112.365 < 2e-16 ***
## category_code_LT01_2_count 0.81217 0.07868 10.323 < 2e-16 ***
## category_code_LT01_3_count 0.49199 0.10992 4.476 9.47e-06 ***
## category_code_LT01_5_count 0.96200 0.06357 15.132 < 2e-16 ***
## category_code_LT01_8_count -0.11778 0.27839 -0.423 0.672
## category_code_LT01_12_count 0.06506 0.21028 0.309 0.757
## category_code_LT01_14_count 0.37034 0.33046 1.121 0.263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6154, Adjusted R-squared: 0.6107
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609720461425417
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0329 -0.7650 0.0045 0.8610 3.8255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97549 0.08886 112.258 < 2e-16 ***
## category_code_LT01_2_count 0.82613 0.07798 10.594 < 2e-16 ***
## category_code_LT01_3_count 0.49036 0.11122 4.409 1.28e-05 ***
## category_code_LT01_5_count 0.97024 0.06325 15.341 < 2e-16 ***
## category_code_LT01_8_count -0.11315 0.27873 -0.406 0.685
## category_code_LT01_12_count 0.08419 0.20995 0.401 0.689
## category_code_LT01_15_count 0.03560 0.76731 0.046 0.963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6097
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.609765337109704
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0324 -0.7646 -0.0028 0.8586 3.8259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97502 0.08887 112.241 < 2e-16 ***
## category_code_LT01_2_count 0.82774 0.07793 10.621 < 2e-16 ***
## category_code_LT01_3_count 0.49332 0.11044 4.467 9.85e-06 ***
## category_code_LT01_5_count 0.97019 0.06323 15.344 < 2e-16 ***
## category_code_LT01_8_count -0.10971 0.27903 -0.393 0.694
## category_code_LT01_12_count 0.08250 0.20995 0.393 0.695
## category_code_LT01_16_count -0.28921 1.19455 -0.242 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611122043700035
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0303 -0.7685 0.0222 0.8716 3.8220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97898 0.08876 112.422 < 2e-16 ***
## category_code_LT01_2_count 0.80982 0.07773 10.419 < 2e-16 ***
## category_code_LT01_3_count 0.49079 0.10963 4.477 9.43e-06 ***
## category_code_LT01_5_count 0.96145 0.06339 15.168 < 2e-16 ***
## category_code_LT01_8_count -0.10238 0.27850 -0.368 0.713
## category_code_LT01_13_count 0.19242 0.24630 0.781 0.435
## category_code_LT01_14_count 0.37562 0.32924 1.141 0.254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.610096991122711
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0327 -0.7742 0.0102 0.8858 3.8257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97521 0.08882 112.308 < 2e-16 ***
## category_code_LT01_2_count 0.82507 0.07684 10.738 < 2e-16 ***
## category_code_LT01_3_count 0.48925 0.11095 4.410 1.27e-05 ***
## category_code_LT01_5_count 0.97034 0.06301 15.399 < 2e-16 ***
## category_code_LT01_8_count -0.09653 0.27882 -0.346 0.729
## category_code_LT01_13_count 0.19692 0.24709 0.797 0.426
## category_code_LT01_15_count 0.06551 0.76816 0.085 0.932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610129242729328
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0322 -0.7643 0.0087 0.8804 3.8262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97476 0.08883 112.289 < 2e-16 ***
## category_code_LT01_2_count 0.82679 0.07678 10.769 < 2e-16 ***
## category_code_LT01_3_count 0.49262 0.11015 4.472 9.63e-06 ***
## category_code_LT01_5_count 0.97024 0.06300 15.401 < 2e-16 ***
## category_code_LT01_8_count -0.09362 0.27909 -0.335 0.737
## category_code_LT01_13_count 0.19323 0.24684 0.783 0.434
## category_code_LT01_16_count -0.26144 1.19467 -0.219 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610638779660625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0323 -0.7691 0.0206 0.8478 3.8215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.979499 0.088820 112.357 < 2e-16 ***
## category_code_LT01_2_count 0.816707 0.077448 10.545 < 2e-16 ***
## category_code_LT01_3_count 0.494506 0.110708 4.467 9.87e-06 ***
## category_code_LT01_5_count 0.963636 0.063384 15.203 < 2e-16 ***
## category_code_LT01_8_count -0.114671 0.278243 -0.412 0.680
## category_code_LT01_14_count 0.378446 0.329494 1.149 0.251
## category_code_LT01_15_count 0.009439 0.766285 0.012 0.990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610665708514393
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0320 -0.7687 0.0170 0.8435 3.8218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97912 0.08884 112.332 < 2e-16 ***
## category_code_LT01_2_count 0.81786 0.07749 10.555 < 2e-16 ***
## category_code_LT01_3_count 0.49635 0.10995 4.514 7.95e-06 ***
## category_code_LT01_5_count 0.96368 0.06336 15.209 < 2e-16 ***
## category_code_LT01_8_count -0.11212 0.27856 -0.402 0.687
## category_code_LT01_14_count 0.37486 0.33001 1.136 0.257
## category_code_LT01_16_count -0.22069 1.19487 -0.185 0.854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6154, Adjusted R-squared: 0.6107
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.60964303058453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0342 -0.7648 0.0046 0.8588 3.8258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97520 0.08889 112.221 < 2e-16 ***
## category_code_LT01_2_count 0.83394 0.07649 10.903 < 2e-16 ***
## category_code_LT01_3_count 0.49648 0.11128 4.462 1.01e-05 ***
## category_code_LT01_5_count 0.97251 0.06299 15.440 < 2e-16 ***
## category_code_LT01_8_count -0.10550 0.27888 -0.378 0.705
## category_code_LT01_15_count 0.01739 0.76810 0.023 0.982
## category_code_LT01_16_count -0.30102 1.19582 -0.252 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6096
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.619854003878213
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0129 -0.7593 0.0318 0.8244 4.1489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96976 0.09089 109.695 < 2e-16 ***
## category_code_LT01_2_count 0.65391 0.09011 7.257 1.56e-12 ***
## category_code_LT01_3_count 0.35050 0.11579 3.027 0.0026 **
## category_code_LT01_5_count 0.94963 0.06169 15.394 < 2e-16 ***
## category_code_LT01_9_count 0.31189 0.22951 1.359 0.1748
## category_code_LT01_10_count 0.05955 0.11480 0.519 0.6042
## category_code_LT01_11_count 0.38383 0.11688 3.284 0.0011 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6199
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.611614131631195
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0095 -0.7490 0.0333 0.8887 3.8415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95941 0.09181 108.478 < 2e-16 ***
## category_code_LT01_2_count 0.80585 0.07865 10.246 < 2e-16 ***
## category_code_LT01_3_count 0.45489 0.11257 4.041 6.18e-05 ***
## category_code_LT01_5_count 0.95970 0.06254 15.344 < 2e-16 ***
## category_code_LT01_9_count 0.33976 0.23184 1.466 0.143
## category_code_LT01_10_count 0.05767 0.11609 0.497 0.620
## category_code_LT01_12_count 0.07807 0.20930 0.373 0.709
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6163, Adjusted R-squared: 0.6116
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.612139821803813
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0096 -0.7491 0.0347 0.8949 3.8414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95955 0.09175 108.553 < 2e-16 ***
## category_code_LT01_2_count 0.80309 0.07760 10.349 < 2e-16 ***
## category_code_LT01_3_count 0.45325 0.11231 4.036 6.32e-05 ***
## category_code_LT01_5_count 0.95966 0.06225 15.416 < 2e-16 ***
## category_code_LT01_9_count 0.35308 0.23218 1.521 0.129
## category_code_LT01_10_count 0.05553 0.11602 0.479 0.632
## category_code_LT01_13_count 0.22086 0.24619 0.897 0.370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.612210957099664
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0143 -0.7573 0.0314 0.8856 3.8332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96774 0.09217 108.151 < 2e-16 ***
## category_code_LT01_2_count 0.80026 0.07794 10.268 < 2e-16 ***
## category_code_LT01_3_count 0.46314 0.11231 4.124 4.38e-05 ***
## category_code_LT01_5_count 0.95461 0.06268 15.230 < 2e-16 ***
## category_code_LT01_9_count 0.32748 0.23200 1.412 0.159
## category_code_LT01_10_count 0.03463 0.11876 0.292 0.771
## category_code_LT01_14_count 0.31939 0.33761 0.946 0.345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.6122
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.611506955291108
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0112 -0.7525 0.0364 0.8747 3.8415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95950 0.09185 108.433 < 2e-16 ***
## category_code_LT01_2_count 0.81138 0.07727 10.500 < 2e-16 ***
## category_code_LT01_3_count 0.45706 0.11331 4.034 6.37e-05 ***
## category_code_LT01_5_count 0.96208 0.06226 15.454 < 2e-16 ***
## category_code_LT01_9_count 0.34006 0.23211 1.465 0.144
## category_code_LT01_10_count 0.05848 0.11629 0.503 0.615
## category_code_LT01_15_count 0.04616 0.76737 0.060 0.952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6115
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.611577916451439
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0105 -0.7489 0.0296 0.8682 3.8423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95871 0.09184 108.435 < 2e-16 ***
## category_code_LT01_2_count 0.81317 0.07720 10.534 < 2e-16 ***
## category_code_LT01_3_count 0.46056 0.11259 4.091 5.03e-05 ***
## category_code_LT01_5_count 0.96210 0.06224 15.458 < 2e-16 ***
## category_code_LT01_9_count 0.34035 0.23186 1.468 0.143
## category_code_LT01_10_count 0.05962 0.11607 0.514 0.608
## category_code_LT01_16_count -0.36362 1.19031 -0.305 0.760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6163, Adjusted R-squared: 0.6116
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.619773368699171
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7721 0.0180 0.8183 4.1153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98262 0.08775 113.758 < 2e-16 ***
## category_code_LT01_2_count 0.65861 0.09005 7.314 1.07e-12 ***
## category_code_LT01_3_count 0.36047 0.11432 3.153 0.00171 **
## category_code_LT01_5_count 0.95168 0.06193 15.367 < 2e-16 ***
## category_code_LT01_9_count 0.32257 0.22845 1.412 0.15858
## category_code_LT01_11_count 0.39543 0.12039 3.285 0.00109 **
## category_code_LT01_12_count -0.08658 0.21321 -0.406 0.68485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6198
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62002088411781
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0234 -0.7711 0.0237 0.8213 4.1325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98151 0.08772 113.792 < 2e-16 ***
## category_code_LT01_2_count 0.65238 0.09014 7.237 1.78e-12 ***
## category_code_LT01_3_count 0.35761 0.11434 3.128 0.00187 **
## category_code_LT01_5_count 0.94788 0.06172 15.358 < 2e-16 ***
## category_code_LT01_9_count 0.33393 0.22885 1.459 0.14517
## category_code_LT01_11_count 0.37807 0.11714 3.228 0.00133 **
## category_code_LT01_13_count 0.16999 0.24414 0.696 0.48656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.620217598622894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0235 -0.7702 0.0300 0.8158 4.1325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98485 0.08775 113.786 < 2e-16 ***
## category_code_LT01_2_count 0.64805 0.09047 7.163 2.91e-12 ***
## category_code_LT01_3_count 0.36279 0.11429 3.174 0.00160 **
## category_code_LT01_5_count 0.94322 0.06209 15.191 < 2e-16 ***
## category_code_LT01_9_count 0.30925 0.22891 1.351 0.17733
## category_code_LT01_11_count 0.37804 0.11701 3.231 0.00132 **
## category_code_LT01_14_count 0.28095 0.32672 0.860 0.39027
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.61964587669216
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0251 -0.7716 0.0184 0.8138 4.1195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98206 0.08776 113.744 < 2e-16 ***
## category_code_LT01_2_count 0.65690 0.09007 7.293 1.22e-12 ***
## category_code_LT01_3_count 0.36034 0.11534 3.124 0.00189 **
## category_code_LT01_5_count 0.94948 0.06172 15.384 < 2e-16 ***
## category_code_LT01_9_count 0.32337 0.22867 1.414 0.15796
## category_code_LT01_11_count 0.38379 0.11698 3.281 0.00111 **
## category_code_LT01_15_count -0.01256 0.75820 -0.017 0.98679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6196
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.619663085724952
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0249 -0.7714 0.0182 0.8131 4.1164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98179 0.08777 113.722 < 2e-16 ***
## category_code_LT01_2_count 0.65787 0.09023 7.291 1.24e-12 ***
## category_code_LT01_3_count 0.36162 0.11479 3.150 0.00173 **
## category_code_LT01_5_count 0.94956 0.06171 15.389 < 2e-16 ***
## category_code_LT01_9_count 0.32410 0.22851 1.418 0.15673
## category_code_LT01_11_count 0.38293 0.11703 3.272 0.00114 **
## category_code_LT01_16_count -0.17679 1.17884 -0.150 0.88085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6197
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.612062650957824
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0192 -0.7727 0.0186 0.9020 3.8301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97082 0.08857 112.573 < 2e-16 ***
## category_code_LT01_2_count 0.79995 0.07899 10.127 < 2e-16 ***
## category_code_LT01_3_count 0.45893 0.11114 4.129 4.28e-05 ***
## category_code_LT01_5_count 0.95728 0.06255 15.303 < 2e-16 ***
## category_code_LT01_9_count 0.36409 0.23105 1.576 0.116
## category_code_LT01_12_count 0.07581 0.20918 0.362 0.717
## category_code_LT01_13_count 0.22220 0.24618 0.903 0.367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.612217351117435
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0198 -0.7644 0.0235 0.8718 3.8261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97486 0.08862 112.555 < 2e-16 ***
## category_code_LT01_2_count 0.79654 0.07930 10.045 < 2e-16 ***
## category_code_LT01_3_count 0.46605 0.11099 4.199 3.19e-05 ***
## category_code_LT01_5_count 0.95234 0.06290 15.141 < 2e-16 ***
## category_code_LT01_9_count 0.33363 0.23119 1.443 0.150
## category_code_LT01_12_count 0.06400 0.20974 0.305 0.760
## category_code_LT01_14_count 0.33250 0.33071 1.005 0.315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.6122
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.611427284518661
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0214 -0.7668 0.0304 0.8668 3.8296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97140 0.08865 112.486 < 2e-16 ***
## category_code_LT01_2_count 0.80774 0.07877 10.255 < 2e-16 ***
## category_code_LT01_3_count 0.46231 0.11233 4.116 4.53e-05 ***
## category_code_LT01_5_count 0.95958 0.06257 15.337 < 2e-16 ***
## category_code_LT01_9_count 0.35196 0.23096 1.524 0.128
## category_code_LT01_12_count 0.08165 0.20934 0.390 0.697
## category_code_LT01_15_count 0.07858 0.76619 0.103 0.918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6114
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.61148336746388
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0209 -0.7604 0.0286 0.8729 3.8301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97085 0.08865 112.472 < 2e-16 ***
## category_code_LT01_2_count 0.80991 0.07867 10.295 < 2e-16 ***
## category_code_LT01_3_count 0.46659 0.11145 4.187 3.36e-05 ***
## category_code_LT01_5_count 0.95960 0.06256 15.340 < 2e-16 ***
## category_code_LT01_9_count 0.35202 0.23079 1.525 0.128
## category_code_LT01_12_count 0.07951 0.20932 0.380 0.704
## category_code_LT01_16_count -0.33969 1.19063 -0.285 0.776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6115
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.612780472949136
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0192 -0.7709 0.0233 0.8880 3.8264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97456 0.08856 112.635 < 2e-16 ***
## category_code_LT01_2_count 0.79246 0.07842 10.105 < 2e-16 ***
## category_code_LT01_3_count 0.46350 0.11071 4.187 3.36e-05 ***
## category_code_LT01_5_count 0.95180 0.06265 15.192 < 2e-16 ***
## category_code_LT01_9_count 0.34635 0.23149 1.496 0.135
## category_code_LT01_13_count 0.22092 0.24588 0.898 0.369
## category_code_LT01_14_count 0.33623 0.32942 1.021 0.308
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.6128
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.611977236253908
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0212 -0.7743 0.0281 0.9063 3.8298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97112 0.08858 112.563 < 2e-16 ***
## category_code_LT01_2_count 0.80460 0.07773 10.352 < 2e-16 ***
## category_code_LT01_3_count 0.45951 0.11208 4.100 4.84e-05 ***
## category_code_LT01_5_count 0.95964 0.06227 15.411 < 2e-16 ***
## category_code_LT01_9_count 0.36568 0.23129 1.581 0.115
## category_code_LT01_13_count 0.22716 0.24664 0.921 0.357
## category_code_LT01_15_count 0.11691 0.76703 0.152 0.879
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.612
## F-statistic: 131.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.612009972532536
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0206 -0.7610 0.0216 0.8948 3.8304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97060 0.08859 112.545 < 2e-16 ***
## category_code_LT01_2_count 0.80697 0.07761 10.398 < 2e-16 ***
## category_code_LT01_3_count 0.46438 0.11118 4.177 3.5e-05 ***
## category_code_LT01_5_count 0.95959 0.06226 15.412 < 2e-16 ***
## category_code_LT01_9_count 0.36486 0.23108 1.579 0.115
## category_code_LT01_13_count 0.22184 0.24636 0.900 0.368
## category_code_LT01_16_count -0.30276 1.19065 -0.254 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.612
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.612147470861276
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0214 -0.7647 0.0224 0.8710 3.8258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97516 0.08863 112.551 < 2e-16 ***
## category_code_LT01_2_count 0.80070 0.07811 10.251 < 2e-16 ***
## category_code_LT01_3_count 0.46767 0.11185 4.181 3.44e-05 ***
## category_code_LT01_5_count 0.95413 0.06267 15.225 < 2e-16 ***
## category_code_LT01_9_count 0.33377 0.23141 1.442 0.150
## category_code_LT01_14_count 0.34015 0.32974 1.032 0.303
## category_code_LT01_15_count 0.05205 0.76542 0.068 0.946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.61218687140288
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0210 -0.7642 0.0219 0.8646 3.8263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97467 0.08864 112.528 < 2e-16 ***
## category_code_LT01_2_count 0.80241 0.07809 10.275 < 2e-16 ***
## category_code_LT01_3_count 0.47083 0.11098 4.242 2.64e-05 ***
## category_code_LT01_5_count 0.95421 0.06266 15.229 < 2e-16 ***
## category_code_LT01_9_count 0.33419 0.23124 1.445 0.149
## category_code_LT01_14_count 0.33601 0.33024 1.017 0.309
## category_code_LT01_16_count -0.27814 1.19133 -0.233 0.815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.6122
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.611373948891376
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0228 -0.7717 0.0222 0.8790 3.8298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97111 0.08867 112.456 < 2e-16 ***
## category_code_LT01_2_count 0.81552 0.07728 10.553 < 2e-16 ***
## category_code_LT01_3_count 0.46873 0.11237 4.171 3.58e-05 ***
## category_code_LT01_5_count 0.96204 0.06227 15.450 < 2e-16 ***
## category_code_LT01_9_count 0.35265 0.23100 1.527 0.127
## category_code_LT01_15_count 0.05942 0.76692 0.077 0.938
## category_code_LT01_16_count -0.34694 1.19187 -0.291 0.771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6114
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.618574047235468
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0185 -0.7592 0.0052 0.8350 4.0977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96968 0.09104 109.507 < 2e-16 ***
## category_code_LT01_2_count 0.66864 0.08985 7.442 4.49e-13 ***
## category_code_LT01_3_count 0.36923 0.11517 3.206 0.001434 **
## category_code_LT01_5_count 0.95786 0.06187 15.482 < 2e-16 ***
## category_code_LT01_10_count 0.07626 0.11450 0.666 0.505695
## category_code_LT01_11_count 0.40229 0.12050 3.339 0.000906 ***
## category_code_LT01_12_count -0.09379 0.21362 -0.439 0.660819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6232, Adjusted R-squared: 0.6186
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.618689489601193
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0157 -0.7589 0.0058 0.8301 4.1099
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96937 0.09102 109.523 < 2e-16 ***
## category_code_LT01_2_count 0.66360 0.08992 7.380 6.8e-13 ***
## category_code_LT01_3_count 0.36797 0.11517 3.195 0.00149 **
## category_code_LT01_5_count 0.95433 0.06166 15.476 < 2e-16 ***
## category_code_LT01_10_count 0.07305 0.11447 0.638 0.52368
## category_code_LT01_11_count 0.38505 0.11724 3.284 0.00110 **
## category_code_LT01_13_count 0.14266 0.24412 0.584 0.55925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6233, Adjusted R-squared: 0.6187
## F-statistic: 135.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.618964571923137
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0193 -0.7636 0.0172 0.8257 4.1055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97657 0.09140 109.155 < 2e-16 ***
## category_code_LT01_2_count 0.65874 0.09024 7.300 1.17e-12 ***
## category_code_LT01_3_count 0.37465 0.11530 3.249 0.00124 **
## category_code_LT01_5_count 0.94904 0.06210 15.283 < 2e-16 ***
## category_code_LT01_10_count 0.05305 0.11732 0.452 0.65133
## category_code_LT01_11_count 0.38375 0.11715 3.276 0.00113 **
## category_code_LT01_14_count 0.27932 0.33477 0.834 0.40447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6236, Adjusted R-squared: 0.619
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.618434214644894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0162 -0.7587 0.0086 0.8367 4.0996
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96916 0.09108 109.456 < 2e-16 ***
## category_code_LT01_2_count 0.66725 0.08985 7.426 4.97e-13 ***
## category_code_LT01_3_count 0.37065 0.11596 3.196 0.001482 **
## category_code_LT01_5_count 0.95536 0.06167 15.492 < 2e-16 ***
## category_code_LT01_10_count 0.07556 0.11467 0.659 0.510239
## category_code_LT01_11_count 0.39005 0.11708 3.332 0.000929 ***
## category_code_LT01_15_count -0.08585 0.76009 -0.113 0.910121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.618439490955633
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0163 -0.7586 0.0024 0.8364 4.0986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96907 0.09108 109.450 < 2e-16 ***
## category_code_LT01_2_count 0.66782 0.09005 7.416 5.33e-13 ***
## category_code_LT01_3_count 0.37055 0.11563 3.205 0.001440 **
## category_code_LT01_5_count 0.95559 0.06165 15.499 < 2e-16 ***
## category_code_LT01_10_count 0.07514 0.11449 0.656 0.511970
## category_code_LT01_11_count 0.38890 0.11713 3.320 0.000967 ***
## category_code_LT01_16_count -0.16509 1.18083 -0.140 0.888870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.610407477170961
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0125 -0.7492 0.0093 0.8721 3.8420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95899 0.09195 108.306 < 2e-16 ***
## category_code_LT01_2_count 0.81599 0.07838 10.410 < 2e-16 ***
## category_code_LT01_3_count 0.47385 0.11180 4.238 2.69e-05 ***
## category_code_LT01_5_count 0.96467 0.06251 15.431 < 2e-16 ***
## category_code_LT01_10_count 0.07198 0.11575 0.622 0.534
## category_code_LT01_12_count 0.07235 0.20970 0.345 0.730
## category_code_LT01_13_count 0.19400 0.24630 0.788 0.431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.610703854414142
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0172 -0.7573 0.0225 0.8633 3.8332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96771 0.09235 107.939 < 2e-16 ***
## category_code_LT01_2_count 0.81110 0.07875 10.299 < 2e-16 ***
## category_code_LT01_3_count 0.48231 0.11181 4.314 1.94e-05 ***
## category_code_LT01_5_count 0.95876 0.06292 15.239 < 2e-16 ***
## category_code_LT01_10_count 0.04824 0.11858 0.407 0.684
## category_code_LT01_12_count 0.06087 0.21016 0.290 0.772
## category_code_LT01_14_count 0.33784 0.33875 0.997 0.319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6154, Adjusted R-squared: 0.6107
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609915231794597
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0136 -0.7494 0.0082 0.8491 3.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.958854 0.092036 108.206 < 2e-16 ***
## category_code_LT01_2_count 0.822630 0.078182 10.522 < 2e-16 ***
## category_code_LT01_3_count 0.476988 0.112776 4.229 2.79e-05 ***
## category_code_LT01_5_count 0.966370 0.062526 15.456 < 2e-16 ***
## category_code_LT01_10_count 0.074275 0.115994 0.640 0.522
## category_code_LT01_12_count 0.076868 0.209845 0.366 0.714
## category_code_LT01_15_count 0.002858 0.768455 0.004 0.997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6099
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.609975656365147
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0131 -0.7478 0.0078 0.8447 3.8427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95825 0.09203 108.208 < 2e-16 ***
## category_code_LT01_2_count 0.82412 0.07815 10.545 < 2e-16 ***
## category_code_LT01_3_count 0.47949 0.11214 4.276 2.29e-05 ***
## category_code_LT01_5_count 0.96651 0.06251 15.461 < 2e-16 ***
## category_code_LT01_10_count 0.07500 0.11581 0.648 0.518
## category_code_LT01_12_count 0.07529 0.20982 0.359 0.720
## category_code_LT01_16_count -0.32909 1.19309 -0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.61
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611135883099342
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0173 -0.7575 0.0238 0.8654 3.8330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96798 0.09229 108.004 < 2e-16 ***
## category_code_LT01_2_count 0.80840 0.07781 10.390 < 2e-16 ***
## category_code_LT01_3_count 0.48140 0.11150 4.318 1.91e-05 ***
## category_code_LT01_5_count 0.95857 0.06268 15.294 < 2e-16 ***
## category_code_LT01_10_count 0.04631 0.11855 0.391 0.696
## category_code_LT01_13_count 0.19516 0.24598 0.793 0.428
## category_code_LT01_14_count 0.34410 0.33758 1.019 0.309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.610314616098538
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0141 -0.7495 0.0167 0.8538 3.8419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95904 0.09199 108.263 < 2e-16 ***
## category_code_LT01_2_count 0.82108 0.07706 10.655 < 2e-16 ***
## category_code_LT01_3_count 0.47599 0.11251 4.231 2.78e-05 ***
## category_code_LT01_5_count 0.96683 0.06223 15.536 < 2e-16 ***
## category_code_LT01_10_count 0.07278 0.11593 0.628 0.530
## category_code_LT01_13_count 0.19702 0.24675 0.798 0.425
## category_code_LT01_15_count 0.03453 0.76936 0.045 0.964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.615, Adjusted R-squared: 0.6103
## F-statistic: 130.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610362047262034
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0135 -0.7502 0.0152 0.8472 3.8426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95840 0.09198 108.263 < 2e-16 ***
## category_code_LT01_2_count 0.82266 0.07702 10.681 < 2e-16 ***
## category_code_LT01_3_count 0.47886 0.11186 4.281 2.24e-05 ***
## category_code_LT01_5_count 0.96689 0.06222 15.540 < 2e-16 ***
## category_code_LT01_10_count 0.07374 0.11574 0.637 0.524
## category_code_LT01_13_count 0.19343 0.24649 0.785 0.433
## category_code_LT01_16_count -0.29663 1.19338 -0.249 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610637510151466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0184 -0.7574 0.0160 0.8571 3.8331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96784 0.09238 107.905 < 2e-16 ***
## category_code_LT01_2_count 0.81546 0.07752 10.519 < 2e-16 ***
## category_code_LT01_3_count 0.48502 0.11244 4.314 1.94e-05 ***
## category_code_LT01_5_count 0.96036 0.06269 15.320 < 2e-16 ***
## category_code_LT01_10_count 0.04872 0.11877 0.410 0.682
## category_code_LT01_14_count 0.34536 0.33780 1.022 0.307
## category_code_LT01_15_count -0.01107 0.76745 -0.014 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610675459598627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0180 -0.7568 0.0125 0.8548 3.8337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96727 0.09239 107.884 < 2e-16 ***
## category_code_LT01_2_count 0.81664 0.07756 10.529 < 2e-16 ***
## category_code_LT01_3_count 0.48664 0.11179 4.353 1.63e-05 ***
## category_code_LT01_5_count 0.96057 0.06268 15.326 < 2e-16 ***
## category_code_LT01_10_count 0.04954 0.11865 0.418 0.676
## category_code_LT01_14_count 0.34045 0.33850 1.006 0.315
## category_code_LT01_16_count -0.26181 1.19416 -0.219 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6154, Adjusted R-squared: 0.6107
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609873740079438
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0145 -0.7477 0.0100 0.8392 3.8428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95813 0.09207 108.159 < 2e-16 ***
## category_code_LT01_2_count 0.82994 0.07672 10.817 < 2e-16 ***
## category_code_LT01_3_count 0.48286 0.11284 4.279 2.26e-05 ***
## category_code_LT01_5_count 0.96870 0.06223 15.567 < 2e-16 ***
## category_code_LT01_10_count 0.07637 0.11598 0.658 0.511
## category_code_LT01_15_count -0.01658 0.76921 -0.022 0.983
## category_code_LT01_16_count -0.34192 1.19442 -0.286 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6099
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.618515058920307
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0332 -0.7747 -0.0111 0.8193 4.0675
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98516 0.08788 113.628 < 2e-16 ***
## category_code_LT01_2_count 0.66952 0.08980 7.455 4.08e-13 ***
## category_code_LT01_3_count 0.38098 0.11347 3.358 0.000847 ***
## category_code_LT01_5_count 0.95667 0.06191 15.453 < 2e-16 ***
## category_code_LT01_11_count 0.39733 0.12074 3.291 0.001071 **
## category_code_LT01_12_count -0.09124 0.21357 -0.427 0.669398
## category_code_LT01_13_count 0.14801 0.24412 0.606 0.544582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6231, Adjusted R-squared: 0.6185
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.618989953741453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0325 -0.7745 0.0069 0.8127 4.0749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98871 0.08788 113.667 < 2e-16 ***
## category_code_LT01_2_count 0.66240 0.09020 7.343 8.72e-13 ***
## category_code_LT01_3_count 0.38456 0.11339 3.392 0.000751 ***
## category_code_LT01_5_count 0.95068 0.06226 15.269 < 2e-16 ***
## category_code_LT01_11_count 0.39697 0.12053 3.294 0.001061 **
## category_code_LT01_12_count -0.10419 0.21392 -0.487 0.626452
## category_code_LT01_14_count 0.32389 0.32716 0.990 0.322669
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6236, Adjusted R-squared: 0.619
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.618235609975859
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0342 -0.7698 -0.0103 0.8168 4.0562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98550 0.08791 113.591 < 2e-16 ***
## category_code_LT01_2_count 0.67333 0.08975 7.502 2.97e-13 ***
## category_code_LT01_3_count 0.38381 0.11441 3.355 0.000856 ***
## category_code_LT01_5_count 0.95776 0.06191 15.470 < 2e-16 ***
## category_code_LT01_11_count 0.40230 0.12064 3.335 0.000919 ***
## category_code_LT01_12_count -0.09033 0.21376 -0.423 0.672805
## category_code_LT01_15_count -0.06770 0.75945 -0.089 0.929005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.618243102191219
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0342 -0.7701 -0.0153 0.8156 4.0550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98532 0.08792 113.569 < 2e-16 ***
## category_code_LT01_2_count 0.67392 0.08995 7.492 3.17e-13 ***
## category_code_LT01_3_count 0.38391 0.11396 3.369 0.000815 ***
## category_code_LT01_5_count 0.95795 0.06190 15.476 < 2e-16 ***
## category_code_LT01_11_count 0.40125 0.12064 3.326 0.000947 ***
## category_code_LT01_12_count -0.09010 0.21365 -0.422 0.673407
## category_code_LT01_16_count -0.15660 1.18102 -0.133 0.894564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.619080992254827
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0289 -0.7730 0.0152 0.8157 4.0888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98754 0.08785 113.682 < 2e-16 ***
## category_code_LT01_2_count 0.65731 0.09030 7.279 1.34e-12 ***
## category_code_LT01_3_count 0.38259 0.11340 3.374 0.00080 ***
## category_code_LT01_5_count 0.94715 0.06210 15.252 < 2e-16 ***
## category_code_LT01_11_count 0.37849 0.11738 3.224 0.00135 **
## category_code_LT01_13_count 0.14525 0.24392 0.595 0.55179
## category_code_LT01_14_count 0.31172 0.32636 0.955 0.33998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.6183741992766
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7741 0.0047 0.8323 4.0713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98457 0.08788 113.611 < 2e-16 ***
## category_code_LT01_2_count 0.66787 0.08983 7.435 4.69e-13 ***
## category_code_LT01_3_count 0.38118 0.11444 3.331 0.000931 ***
## category_code_LT01_5_count 0.95436 0.06170 15.467 < 2e-16 ***
## category_code_LT01_11_count 0.38521 0.11737 3.282 0.001104 **
## category_code_LT01_13_count 0.14617 0.24466 0.597 0.550494
## category_code_LT01_15_count -0.02666 0.76045 -0.035 0.972049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.618380990745459
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0309 -0.7740 0.0034 0.8324 4.0697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98442 0.08790 113.590 < 2e-16 ***
## category_code_LT01_2_count 0.66846 0.09003 7.425 5.02e-13 ***
## category_code_LT01_3_count 0.38172 0.11398 3.349 0.000873 ***
## category_code_LT01_5_count 0.95446 0.06169 15.471 < 2e-16 ***
## category_code_LT01_11_count 0.38457 0.11739 3.276 0.001128 **
## category_code_LT01_13_count 0.14567 0.24437 0.596 0.551385
## category_code_LT01_16_count -0.11798 1.18177 -0.100 0.920516
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.623, Adjusted R-squared: 0.6184
## F-statistic: 135.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.618812466356322
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7751 0.0122 0.8224 4.0775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98789 0.08789 113.647 < 2e-16 ***
## category_code_LT01_2_count 0.66102 0.09023 7.326 9.82e-13 ***
## category_code_LT01_3_count 0.38545 0.11434 3.371 0.000808 ***
## category_code_LT01_5_count 0.94819 0.06211 15.267 < 2e-16 ***
## category_code_LT01_11_count 0.38347 0.11722 3.271 0.001146 **
## category_code_LT01_14_count 0.31351 0.32652 0.960 0.337460
## category_code_LT01_15_count -0.06982 0.75853 -0.092 0.926694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6188
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.618809874437762
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0301 -0.7750 0.0116 0.8224 4.0779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98781 0.08791 113.620 < 2e-16 ***
## category_code_LT01_2_count 0.66123 0.09049 7.307 1.11e-12 ***
## category_code_LT01_3_count 0.38485 0.11388 3.379 0.000784 ***
## category_code_LT01_5_count 0.94840 0.06210 15.273 < 2e-16 ***
## category_code_LT01_11_count 0.38279 0.11727 3.264 0.001174 **
## category_code_LT01_14_count 0.31162 0.32700 0.953 0.341071
## category_code_LT01_16_count -0.08469 1.18190 -0.072 0.942902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6188
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.618109852547518
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0319 -0.7738 -0.0031 0.8228 4.0579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98470 0.08793 113.551 < 2e-16 ***
## category_code_LT01_2_count 0.67241 0.08996 7.474 3.59e-13 ***
## category_code_LT01_3_count 0.38471 0.11497 3.346 0.000882 ***
## category_code_LT01_5_count 0.95558 0.06169 15.489 < 2e-16 ***
## category_code_LT01_11_count 0.38940 0.11724 3.321 0.000962 ***
## category_code_LT01_15_count -0.06115 0.76001 -0.080 0.935905
## category_code_LT01_16_count -0.15330 1.18250 -0.130 0.896906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6181
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611073861310846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0255 -0.7684 0.0080 0.8626 3.8233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97770 0.08873 112.455 < 2e-16 ***
## category_code_LT01_2_count 0.80580 0.07909 10.188 < 2e-16 ***
## category_code_LT01_3_count 0.48708 0.10993 4.431 1.16e-05 ***
## category_code_LT01_5_count 0.95641 0.06290 15.206 < 2e-16 ***
## category_code_LT01_12_count 0.05728 0.21012 0.273 0.785
## category_code_LT01_13_count 0.19570 0.24602 0.795 0.427
## category_code_LT01_14_count 0.36609 0.33027 1.108 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.610107395804311
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0276 -0.7636 0.0069 0.8886 3.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97404 0.08878 112.351 < 2e-16 ***
## category_code_LT01_2_count 0.81918 0.07845 10.441 < 2e-16 ***
## category_code_LT01_3_count 0.48470 0.11126 4.356 1.61e-05 ***
## category_code_LT01_5_count 0.96473 0.06254 15.425 < 2e-16 ***
## category_code_LT01_12_count 0.07649 0.20977 0.365 0.716
## category_code_LT01_13_count 0.19931 0.24679 0.808 0.420
## category_code_LT01_15_count 0.07092 0.76838 0.092 0.926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610140939530825
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7632 0.0037 0.8820 3.8274
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97361 0.08878 112.336 < 2e-16 ***
## category_code_LT01_2_count 0.82110 0.07839 10.474 < 2e-16 ***
## category_code_LT01_3_count 0.48835 0.11047 4.421 1.21e-05 ***
## category_code_LT01_5_count 0.96477 0.06254 15.427 < 2e-16 ***
## category_code_LT01_12_count 0.07483 0.20976 0.357 0.721
## category_code_LT01_13_count 0.19536 0.24656 0.792 0.429
## category_code_LT01_16_count -0.26898 1.19379 -0.225 0.822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610572872949995
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0271 -0.7676 0.0113 0.8429 3.8229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97808 0.08878 112.385 < 2e-16 ***
## category_code_LT01_2_count 0.81247 0.07889 10.298 < 2e-16 ***
## category_code_LT01_3_count 0.49044 0.11106 4.416 1.24e-05 ***
## category_code_LT01_5_count 0.95810 0.06292 15.228 < 2e-16 ***
## category_code_LT01_12_count 0.06192 0.21027 0.294 0.769
## category_code_LT01_14_count 0.36808 0.33056 1.114 0.266
## category_code_LT01_15_count 0.01296 0.76663 0.017 0.987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.61060384852646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0268 -0.7673 0.0084 0.8394 3.8233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97770 0.08880 112.365 < 2e-16 ***
## category_code_LT01_2_count 0.81379 0.07892 10.311 < 2e-16 ***
## category_code_LT01_3_count 0.49255 0.11029 4.466 9.9e-06 ***
## category_code_LT01_5_count 0.95826 0.06291 15.233 < 2e-16 ***
## category_code_LT01_12_count 0.06092 0.21022 0.290 0.772
## category_code_LT01_14_count 0.36444 0.33101 1.101 0.271
## category_code_LT01_16_count -0.23679 1.19381 -0.198 0.843
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609643130680191
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.7635 -0.0073 0.8682 3.8270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97392 0.08884 112.264 < 2e-16 ***
## category_code_LT01_2_count 0.82795 0.07819 10.588 < 2e-16 ***
## category_code_LT01_3_count 0.49184 0.11165 4.405 1.3e-05 ***
## category_code_LT01_5_count 0.96653 0.06255 15.452 < 2e-16 ***
## category_code_LT01_12_count 0.07944 0.20990 0.378 0.705
## category_code_LT01_15_count 0.02208 0.76835 0.029 0.977
## category_code_LT01_16_count -0.31041 1.19488 -0.260 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6096
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.611017800216282
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7675 0.0134 0.8798 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97796 0.08873 112.452 < 2e-16 ***
## category_code_LT01_2_count 0.80943 0.07796 10.383 < 2e-16 ***
## category_code_LT01_3_count 0.48850 0.11076 4.410 1.27e-05 ***
## category_code_LT01_5_count 0.95799 0.06268 15.285 < 2e-16 ***
## category_code_LT01_13_count 0.19844 0.24644 0.805 0.421
## category_code_LT01_14_count 0.37292 0.32930 1.132 0.258
## category_code_LT01_15_count 0.04557 0.76741 0.059 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.611
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.611037401218717
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0266 -0.7672 0.0130 0.8743 3.8233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97761 0.08875 112.430 < 2e-16 ***
## category_code_LT01_2_count 0.81084 0.07799 10.397 < 2e-16 ***
## category_code_LT01_3_count 0.49102 0.10998 4.465 9.96e-06 ***
## category_code_LT01_5_count 0.95807 0.06267 15.288 < 2e-16 ***
## category_code_LT01_13_count 0.19562 0.24620 0.795 0.427
## category_code_LT01_14_count 0.37007 0.32979 1.122 0.262
## category_code_LT01_16_count -0.20077 1.19412 -0.168 0.867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.611
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.61004387459047
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.7634 0.0047 0.8886 3.8271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97385 0.08880 112.322 < 2e-16 ***
## category_code_LT01_2_count 0.82629 0.07706 10.723 < 2e-16 ***
## category_code_LT01_3_count 0.49037 0.11136 4.403 1.31e-05 ***
## category_code_LT01_5_count 0.96704 0.06225 15.534 < 2e-16 ***
## category_code_LT01_13_count 0.19885 0.24703 0.805 0.421
## category_code_LT01_15_count 0.05442 0.76930 0.071 0.944
## category_code_LT01_16_count -0.27509 1.19528 -0.230 0.818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.61
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610537249631333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0281 -0.7675 0.0152 0.8497 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.977931 0.088805 112.358 < 2e-16 ***
## category_code_LT01_2_count 0.818142 0.077699 10.530 < 2e-16 ***
## category_code_LT01_3_count 0.495202 0.111115 4.457 1.03e-05 ***
## category_code_LT01_5_count 0.959876 0.062682 15.313 < 2e-16 ***
## category_code_LT01_14_count 0.372057 0.330045 1.127 0.260
## category_code_LT01_15_count -0.001216 0.767286 -0.002 0.999
## category_code_LT01_16_count -0.244347 1.195085 -0.204 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6105
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.644560877493618
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9649 -0.7222 0.0358 0.8656 3.4877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95288 0.08491 117.210 < 2e-16 ***
## category_code_LT01_2_count 0.46979 0.08987 5.228 2.54e-07 ***
## category_code_LT01_4_count 0.58492 0.09478 6.171 1.42e-09 ***
## category_code_LT01_5_count 0.90477 0.06094 14.847 < 2e-16 ***
## category_code_LT01_6_count 0.31776 0.14954 2.125 0.03409 *
## category_code_LT01_7_count 0.40793 0.15038 2.713 0.00691 **
## category_code_LT01_8_count -0.16793 0.26613 -0.631 0.52832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6489, Adjusted R-squared: 0.6446
## F-statistic: 151.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.645499977164825
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9531 -0.7148 0.0105 0.8681 3.5039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94853 0.08479 117.331 < 2e-16 ***
## category_code_LT01_2_count 0.45746 0.09032 5.065 5.79e-07 ***
## category_code_LT01_4_count 0.57814 0.09478 6.100 2.15e-09 ***
## category_code_LT01_5_count 0.89397 0.06036 14.811 < 2e-16 ***
## category_code_LT01_6_count 0.30544 0.14935 2.045 0.0414 *
## category_code_LT01_7_count 0.38651 0.15075 2.564 0.0106 *
## category_code_LT01_9_count 0.28657 0.21979 1.304 0.1929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.341 on 491 degrees of freedom
## Multiple R-squared: 0.6498, Adjusted R-squared: 0.6455
## F-statistic: 151.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.644426475001558
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9490 -0.7126 0.0268 0.8758 3.4498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94067 0.08795 113.023 < 2e-16 ***
## category_code_LT01_2_count 0.46844 0.09002 5.204 2.88e-07 ***
## category_code_LT01_4_count 0.58361 0.09482 6.155 1.56e-09 ***
## category_code_LT01_5_count 0.89938 0.06031 14.912 < 2e-16 ***
## category_code_LT01_6_count 0.30329 0.15117 2.006 0.04537 *
## category_code_LT01_7_count 0.39941 0.15077 2.649 0.00833 **
## category_code_LT01_10_count 0.05094 0.11052 0.461 0.64507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6487, Adjusted R-squared: 0.6444
## F-statistic: 151.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ new_category_count_col 0.700607290769089
##
## Call:
## lm(formula = single.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7965 -0.6888 0.0159 0.7176 3.8799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.49821 0.08327 114.07 <2e-16 ***
## new_category_count_col 1.31774 0.03862 34.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.232 on 496 degrees of freedom
## Multiple R-squared: 0.7012, Adjusted R-squared: 0.7006
## F-statistic: 1164 on 1 and 496 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.64547532043629
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9638 -0.7383 0.0018 0.8738 3.4914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95634 0.08486 117.329 < 2e-16 ***
## category_code_LT01_2_count 0.42845 0.09556 4.483 9.15e-06 ***
## category_code_LT01_4_count 0.54861 0.09866 5.560 4.43e-08 ***
## category_code_LT01_5_count 0.89823 0.06023 14.914 < 2e-16 ***
## category_code_LT01_6_count 0.29145 0.15022 1.940 0.0529 .
## category_code_LT01_7_count 0.35991 0.15407 2.336 0.0199 *
## category_code_LT01_11_count 0.15388 0.11923 1.291 0.1974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.341 on 491 degrees of freedom
## Multiple R-squared: 0.6498, Adjusted R-squared: 0.6455
## F-statistic: 151.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.644279970061448
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9598 -0.7166 0.0153 0.8643 3.4948
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95128 0.08491 117.198 < 2e-16 ***
## category_code_LT01_2_count 0.47197 0.09055 5.212 2.76e-07 ***
## category_code_LT01_4_count 0.58516 0.09502 6.158 1.53e-09 ***
## category_code_LT01_5_count 0.89972 0.06053 14.864 < 2e-16 ***
## category_code_LT01_6_count 0.31528 0.15020 2.099 0.03632 *
## category_code_LT01_7_count 0.40470 0.15036 2.692 0.00735 **
## category_code_LT01_12_count -0.02027 0.20135 -0.101 0.91986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.644273304628497
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9594 -0.7161 0.0179 0.8652 3.4954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.951262 0.084911 117.196 < 2e-16 ***
## category_code_LT01_2_count 0.470765 0.089945 5.234 2.46e-07 ***
## category_code_LT01_4_count 0.584325 0.095059 6.147 1.64e-09 ***
## category_code_LT01_5_count 0.899179 0.060339 14.902 < 2e-16 ***
## category_code_LT01_6_count 0.313885 0.149499 2.100 0.03628 *
## category_code_LT01_7_count 0.404197 0.151338 2.671 0.00782 **
## category_code_LT01_13_count 0.007257 0.237733 0.031 0.97566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.644293026795142
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9593 -0.7159 0.0198 0.8664 3.4960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95185 0.08498 117.109 < 2e-16 ***
## category_code_LT01_2_count 0.46985 0.09009 5.216 2.71e-07 ***
## category_code_LT01_4_count 0.58231 0.09574 6.082 2.38e-09 ***
## category_code_LT01_5_count 0.89812 0.06068 14.802 < 2e-16 ***
## category_code_LT01_6_count 0.31636 0.15025 2.106 0.0357 *
## category_code_LT01_7_count 0.40315 0.15065 2.676 0.0077 **
## category_code_LT01_14_count 0.05374 0.32025 0.168 0.8668
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.644274016176178
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9595 -0.7163 0.0177 0.8651 3.4953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95130 0.08491 117.194 < 2e-16 ***
## category_code_LT01_2_count 0.47063 0.09005 5.227 2.56e-07 ***
## category_code_LT01_4_count 0.58418 0.09515 6.140 1.71e-09 ***
## category_code_LT01_5_count 0.89927 0.06034 14.904 < 2e-16 ***
## category_code_LT01_6_count 0.31354 0.14959 2.096 0.03659 *
## category_code_LT01_7_count 0.40495 0.15045 2.692 0.00735 **
## category_code_LT01_15_count 0.03188 0.72860 0.044 0.96512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6486, Adjusted R-squared: 0.6443
## F-statistic: 151 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.644536933454359
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9589 -0.7205 0.0204 0.8681 3.4964
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95161 0.08488 117.242 < 2e-16 ***
## category_code_LT01_2_count 0.46326 0.09073 5.106 4.71e-07 ***
## category_code_LT01_4_count 0.58513 0.09479 6.173 1.40e-09 ***
## category_code_LT01_5_count 0.89798 0.06034 14.882 < 2e-16 ***
## category_code_LT01_6_count 0.32220 0.15006 2.147 0.03227 *
## category_code_LT01_7_count 0.40609 0.15032 2.702 0.00714 **
## category_code_LT01_16_count 0.68890 1.14015 0.604 0.54598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.343 on 491 degrees of freedom
## Multiple R-squared: 0.6488, Adjusted R-squared: 0.6445
## F-statistic: 151.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.641006717494333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9610 -0.7424 0.0828 0.9138 3.4908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94779 0.08536 116.543 < 2e-16 ***
## category_code_LT01_2_count 0.48654 0.09013 5.398 1.05e-07 ***
## category_code_LT01_4_count 0.63847 0.09247 6.905 1.56e-11 ***
## category_code_LT01_5_count 0.90655 0.06131 14.787 < 2e-16 ***
## category_code_LT01_6_count 0.30191 0.15039 2.008 0.0452 *
## category_code_LT01_8_count -0.15738 0.26745 -0.588 0.5565
## category_code_LT01_9_count 0.34308 0.22035 1.557 0.1201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6453, Adjusted R-squared: 0.641
## F-statistic: 148.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.639564355585288
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9532 -0.7353 0.0890 0.8712 3.4143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93543 0.08854 112.209 < 2e-16 ***
## category_code_LT01_2_count 0.50029 0.08979 5.571 4.17e-08 ***
## category_code_LT01_4_count 0.64694 0.09246 6.997 8.63e-12 ***
## category_code_LT01_5_count 0.91303 0.06130 14.895 < 2e-16 ***
## category_code_LT01_6_count 0.29611 0.15226 1.945 0.0524 .
## category_code_LT01_8_count -0.14674 0.26788 -0.548 0.5841
## category_code_LT01_10_count 0.07441 0.11096 0.671 0.5028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6396
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.641712177997636
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9735 -0.7463 0.0509 0.8600 3.4770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95810 0.08534 116.685 < 2e-16 ***
## category_code_LT01_2_count 0.43991 0.09594 4.585 5.76e-06 ***
## category_code_LT01_4_count 0.58925 0.09774 6.029 3.25e-09 ***
## category_code_LT01_5_count 0.90970 0.06114 14.880 < 2e-16 ***
## category_code_LT01_6_count 0.28064 0.15104 1.858 0.0638 .
## category_code_LT01_8_count -0.13161 0.26712 -0.493 0.6224
## category_code_LT01_11_count 0.21525 0.11681 1.843 0.0660 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.646, Adjusted R-squared: 0.6417
## F-statistic: 149.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.639239721622827
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9687 -0.7404 0.0903 0.8924 3.4804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95085 0.08554 116.323 < 2e-16 ***
## category_code_LT01_2_count 0.50546 0.09030 5.598 3.62e-08 ***
## category_code_LT01_4_count 0.65004 0.09263 7.018 7.54e-12 ***
## category_code_LT01_5_count 0.91325 0.06150 14.849 < 2e-16 ***
## category_code_LT01_6_count 0.31253 0.15135 2.065 0.0395 *
## category_code_LT01_8_count -0.14275 0.26812 -0.532 0.5947
## category_code_LT01_12_count -0.01748 0.20289 -0.086 0.9314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.63930207639438
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9679 -0.7403 0.0849 0.8807 3.4816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95074 0.08554 116.331 < 2e-16 ***
## category_code_LT01_2_count 0.50310 0.08973 5.607 3.44e-08 ***
## category_code_LT01_4_count 0.64659 0.09291 6.959 1.10e-11 ***
## category_code_LT01_5_count 0.91221 0.06135 14.868 < 2e-16 ***
## category_code_LT01_6_count 0.31213 0.15065 2.072 0.0388 *
## category_code_LT01_8_count -0.13892 0.26837 -0.518 0.6049
## category_code_LT01_13_count 0.07238 0.23822 0.304 0.7614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.63932092649841
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9681 -0.7416 0.0917 0.8852 3.4821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95208 0.08561 116.249 < 2e-16 ***
## category_code_LT01_2_count 0.50214 0.08987 5.588 3.82e-08 ***
## category_code_LT01_4_count 0.64443 0.09359 6.886 1.77e-11 ***
## category_code_LT01_5_count 0.91058 0.06167 14.766 < 2e-16 ***
## category_code_LT01_6_count 0.31664 0.15143 2.091 0.037 *
## category_code_LT01_8_count -0.14531 0.26798 -0.542 0.588
## category_code_LT01_14_count 0.11057 0.32191 0.343 0.731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.639235608302887
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9684 -0.7404 0.0909 0.8933 3.4809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95083 0.08555 116.319 < 2e-16 ***
## category_code_LT01_2_count 0.50472 0.08975 5.624 3.14e-08 ***
## category_code_LT01_4_count 0.64981 0.09270 7.010 7.93e-12 ***
## category_code_LT01_5_count 0.91279 0.06134 14.881 < 2e-16 ***
## category_code_LT01_6_count 0.31153 0.15076 2.066 0.0393 *
## category_code_LT01_8_count -0.14342 0.26797 -0.535 0.5928
## category_code_LT01_15_count -0.03136 0.73335 -0.043 0.9659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.639489962011711
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9683 -0.7429 0.0847 0.8945 3.4814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95126 0.08552 116.365 < 2e-16 ***
## category_code_LT01_2_count 0.49709 0.09046 5.495 6.28e-08 ***
## category_code_LT01_4_count 0.65036 0.09240 7.038 6.59e-12 ***
## category_code_LT01_5_count 0.91195 0.06132 14.872 < 2e-16 ***
## category_code_LT01_6_count 0.31973 0.15127 2.114 0.035 *
## category_code_LT01_8_count -0.15227 0.26827 -0.568 0.571
## category_code_LT01_16_count 0.67855 1.14984 0.590 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6438, Adjusted R-squared: 0.6395
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.64093096142006
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9449 -0.7427 0.0981 0.9045 3.4486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93506 0.08835 112.448 < 2e-16 ***
## category_code_LT01_2_count 0.48506 0.09027 5.374 1.19e-07 ***
## category_code_LT01_4_count 0.63626 0.09252 6.877 1.87e-11 ***
## category_code_LT01_5_count 0.90166 0.06068 14.860 < 2e-16 ***
## category_code_LT01_6_count 0.28746 0.15190 1.892 0.059 .
## category_code_LT01_9_count 0.32651 0.22165 1.473 0.141
## category_code_LT01_10_count 0.05489 0.11144 0.493 0.623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6453, Adjusted R-squared: 0.6409
## F-statistic: 148.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.6430753432705
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9617 -0.7461 0.0868 0.8494 3.4932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95365 0.08517 116.863 < 2e-16 ***
## category_code_LT01_2_count 0.42587 0.09625 4.424 1.19e-05 ***
## category_code_LT01_4_count 0.58006 0.09769 5.938 5.47e-09 ***
## category_code_LT01_5_count 0.89907 0.06051 14.859 < 2e-16 ***
## category_code_LT01_6_count 0.26948 0.15070 1.788 0.0744 .
## category_code_LT01_9_count 0.32001 0.21984 1.456 0.1461
## category_code_LT01_11_count 0.20854 0.11669 1.787 0.0745 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6474, Adjusted R-squared: 0.6431
## F-statistic: 150.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.640761412779696
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9564 -0.7426 0.0861 0.9236 3.4974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94635 0.08535 116.535 < 2e-16 ***
## category_code_LT01_2_count 0.48867 0.09082 5.380 1.15e-07 ***
## category_code_LT01_4_count 0.63842 0.09270 6.887 1.75e-11 ***
## category_code_LT01_5_count 0.90188 0.06089 14.811 < 2e-16 ***
## category_code_LT01_6_count 0.29990 0.15105 1.985 0.0477 *
## category_code_LT01_9_count 0.33876 0.22030 1.538 0.1248
## category_code_LT01_12_count -0.02098 0.20235 -0.104 0.9175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6408
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.640890810178982
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9553 -0.7417 0.0897 0.9293 3.4990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94619 0.08534 116.553 < 2e-16 ***
## category_code_LT01_2_count 0.48517 0.09030 5.373 1.20e-07 ***
## category_code_LT01_4_count 0.63346 0.09301 6.811 2.85e-11 ***
## category_code_LT01_5_count 0.90055 0.06071 14.834 < 2e-16 ***
## category_code_LT01_6_count 0.29957 0.15032 1.993 0.0468 *
## category_code_LT01_9_count 0.34486 0.22071 1.562 0.1188
## category_code_LT01_13_count 0.10302 0.23780 0.433 0.6651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6452, Adjusted R-squared: 0.6409
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.640792757132589
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9558 -0.7395 0.0872 0.9264 3.4987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94719 0.08543 116.442 < 2e-16 ***
## category_code_LT01_2_count 0.48612 0.09035 5.380 1.15e-07 ***
## category_code_LT01_4_count 0.63445 0.09360 6.779 3.49e-11 ***
## category_code_LT01_5_count 0.89986 0.06103 14.744 < 2e-16 ***
## category_code_LT01_6_count 0.30205 0.15115 1.998 0.0462 *
## category_code_LT01_9_count 0.33535 0.22079 1.519 0.1294
## category_code_LT01_14_count 0.07452 0.32191 0.231 0.8170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6408
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.640753593471723
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9560 -0.7401 0.0880 0.9225 3.4980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.946339 0.085354 116.531 < 2e-16 ***
## category_code_LT01_2_count 0.487564 0.090304 5.399 1.04e-07 ***
## category_code_LT01_4_count 0.637830 0.092785 6.874 1.90e-11 ***
## category_code_LT01_5_count 0.901354 0.060701 14.849 < 2e-16 ***
## category_code_LT01_6_count 0.298405 0.150449 1.983 0.0479 *
## category_code_LT01_9_count 0.338726 0.220382 1.537 0.1249
## category_code_LT01_15_count -0.005588 0.732022 -0.008 0.9939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6408
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.640941978977171
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9557 -0.7370 0.0857 0.9326 3.4987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94666 0.08533 116.565 < 2e-16 ***
## category_code_LT01_2_count 0.48140 0.09093 5.294 1.81e-07 ***
## category_code_LT01_4_count 0.63859 0.09249 6.905 1.56e-11 ***
## category_code_LT01_5_count 0.90042 0.06070 14.833 < 2e-16 ***
## category_code_LT01_6_count 0.30555 0.15095 2.024 0.0435 *
## category_code_LT01_9_count 0.33488 0.22038 1.520 0.1293
## category_code_LT01_16_count 0.58196 1.14647 0.508 0.6120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6453, Adjusted R-squared: 0.6409
## F-statistic: 148.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.64182960510133
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9547 -0.7318 0.0395 0.8619 3.4204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94227 0.08835 112.537 < 2e-16 ***
## category_code_LT01_2_count 0.43648 0.09609 4.542 7.01e-06 ***
## category_code_LT01_4_count 0.58602 0.09776 5.995 3.95e-09 ***
## category_code_LT01_5_count 0.90535 0.06046 14.974 < 2e-16 ***
## category_code_LT01_6_count 0.26313 0.15252 1.725 0.0851 .
## category_code_LT01_10_count 0.07029 0.11061 0.635 0.5254
## category_code_LT01_11_count 0.21554 0.11677 1.846 0.0655 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6462, Adjusted R-squared: 0.6418
## F-statistic: 149.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.639354503519139
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9490 -0.7373 0.0884 0.8833 3.4211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93421 0.08855 112.193 < 2e-16 ***
## category_code_LT01_2_count 0.50235 0.09047 5.553 4.61e-08 ***
## category_code_LT01_4_count 0.64693 0.09268 6.980 9.62e-12 ***
## category_code_LT01_5_count 0.90870 0.06086 14.931 < 2e-16 ***
## category_code_LT01_6_count 0.29462 0.15287 1.927 0.0545 .
## category_code_LT01_10_count 0.07362 0.11100 0.663 0.5075
## category_code_LT01_12_count -0.02414 0.20279 -0.119 0.9053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.639416508971582
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9484 -0.7240 0.0839 0.8838 3.4236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93441 0.08854 112.205 < 2e-16 ***
## category_code_LT01_2_count 0.49963 0.08991 5.557 4.51e-08 ***
## category_code_LT01_4_count 0.64325 0.09295 6.921 1.41e-11 ***
## category_code_LT01_5_count 0.90760 0.06067 14.960 < 2e-16 ***
## category_code_LT01_6_count 0.29412 0.15223 1.932 0.0539 .
## category_code_LT01_10_count 0.07228 0.11102 0.651 0.5153
## category_code_LT01_13_count 0.07471 0.23791 0.314 0.7536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6438, Adjusted R-squared: 0.6394
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.639369610952736
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9494 -0.7255 0.0927 0.8933 3.4272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93596 0.08901 111.633 < 2e-16 ***
## category_code_LT01_2_count 0.50001 0.08998 5.557 4.51e-08 ***
## category_code_LT01_4_count 0.64353 0.09358 6.877 1.87e-11 ***
## category_code_LT01_5_count 0.90681 0.06105 14.853 < 2e-16 ***
## category_code_LT01_6_count 0.29688 0.15367 1.932 0.0539 .
## category_code_LT01_10_count 0.06845 0.11402 0.600 0.5486
## category_code_LT01_14_count 0.06163 0.33066 0.186 0.8522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.639350880841648
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9483 -0.7336 0.0883 0.8870 3.4216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93405 0.08857 112.155 < 2e-16 ***
## category_code_LT01_2_count 0.50148 0.08992 5.577 4.05e-08 ***
## category_code_LT01_4_count 0.64686 0.09274 6.975 9.92e-12 ***
## category_code_LT01_5_count 0.90798 0.06067 14.967 < 2e-16 ***
## category_code_LT01_6_count 0.29333 0.15226 1.927 0.0546 .
## category_code_LT01_10_count 0.07410 0.11127 0.666 0.5058
## category_code_LT01_15_count -0.07067 0.73511 -0.096 0.9235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.639546755813342
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9488 -0.7471 0.0846 0.8956 3.4257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93520 0.08854 112.213 < 2e-16 ***
## category_code_LT01_2_count 0.49466 0.09060 5.460 7.59e-08 ***
## category_code_LT01_4_count 0.64702 0.09247 6.997 8.60e-12 ***
## category_code_LT01_5_count 0.90704 0.06067 14.950 < 2e-16 ***
## category_code_LT01_6_count 0.30089 0.15291 1.968 0.0497 *
## category_code_LT01_10_count 0.07023 0.11110 0.632 0.5276
## category_code_LT01_16_count 0.60403 1.14962 0.525 0.5995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6439, Adjusted R-squared: 0.6395
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.641749265372548
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9716 -0.7470 0.0305 0.8489 3.4797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95744 0.08531 116.723 < 2e-16 ***
## category_code_LT01_2_count 0.44180 0.09598 4.603 5.31e-06 ***
## category_code_LT01_4_count 0.58747 0.09772 6.012 3.58e-09 ***
## category_code_LT01_5_count 0.90786 0.06066 14.967 < 2e-16 ***
## category_code_LT01_6_count 0.28354 0.15131 1.874 0.0615 .
## category_code_LT01_11_count 0.23146 0.11993 1.930 0.0542 .
## category_code_LT01_12_count -0.11245 0.20753 -0.542 0.5882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6461, Adjusted R-squared: 0.6417
## F-statistic: 149.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.641580066046346
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9689 -0.7447 0.0613 0.8469 3.4836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95680 0.08532 116.694 < 2e-16 ***
## category_code_LT01_2_count 0.43937 0.09600 4.577 6.00e-06 ***
## category_code_LT01_4_count 0.58618 0.09806 5.978 4.35e-09 ***
## category_code_LT01_5_count 0.90489 0.06050 14.956 < 2e-16 ***
## category_code_LT01_6_count 0.27835 0.15097 1.844 0.0658 .
## category_code_LT01_11_count 0.21529 0.11692 1.841 0.0662 .
## category_code_LT01_13_count 0.05895 0.23735 0.248 0.8040
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6416
## F-statistic: 149.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.641600876206389
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9689 -0.7397 0.0682 0.8544 3.4843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95794 0.08539 116.617 < 2e-16 ***
## category_code_LT01_2_count 0.43827 0.09615 4.558 6.52e-06 ***
## category_code_LT01_4_count 0.58390 0.09875 5.913 6.31e-09 ***
## category_code_LT01_5_count 0.90325 0.06085 14.843 < 2e-16 ***
## category_code_LT01_6_count 0.28211 0.15174 1.859 0.0636 .
## category_code_LT01_11_count 0.21601 0.11681 1.849 0.0650 .
## category_code_LT01_14_count 0.09637 0.32089 0.300 0.7641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6416
## F-statistic: 149.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.641545916674529
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9691 -0.7448 0.0483 0.8518 3.4833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95685 0.08533 116.689 < 2e-16 ***
## category_code_LT01_2_count 0.44056 0.09603 4.588 5.69e-06 ***
## category_code_LT01_4_count 0.58887 0.09791 6.014 3.53e-09 ***
## category_code_LT01_5_count 0.90511 0.06050 14.960 < 2e-16 ***
## category_code_LT01_6_count 0.27803 0.15102 1.841 0.0662 .
## category_code_LT01_11_count 0.21721 0.11689 1.858 0.0637 .
## category_code_LT01_15_count -0.08930 0.73154 -0.122 0.9029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6415
## F-statistic: 149.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.641759354337173
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9688 -0.7433 0.0600 0.8531 3.4840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95720 0.08530 116.727 < 2e-16 ***
## category_code_LT01_2_count 0.43330 0.09672 4.480 9.29e-06 ***
## category_code_LT01_4_count 0.58898 0.09772 6.027 3.28e-09 ***
## category_code_LT01_5_count 0.90416 0.06050 14.945 < 2e-16 ***
## category_code_LT01_6_count 0.28513 0.15153 1.882 0.0605 .
## category_code_LT01_11_count 0.21640 0.11677 1.853 0.0644 .
## category_code_LT01_16_count 0.63459 1.14448 0.554 0.5795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6461, Adjusted R-squared: 0.6418
## F-statistic: 149.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.639114580299692
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.7384 0.0797 0.8821 3.4875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94942 0.08552 116.338 < 2e-16 ***
## category_code_LT01_2_count 0.50486 0.09041 5.584 3.89e-08 ***
## category_code_LT01_4_count 0.64625 0.09311 6.941 1.24e-11 ***
## category_code_LT01_5_count 0.90806 0.06090 14.912 < 2e-16 ***
## category_code_LT01_6_count 0.31067 0.15131 2.053 0.0406 *
## category_code_LT01_12_count -0.02287 0.20287 -0.113 0.9103
## category_code_LT01_13_count 0.08003 0.23798 0.336 0.7368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.639117613155878
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9638 -0.7391 0.0860 0.8876 3.4881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95071 0.08560 116.253 < 2e-16 ***
## category_code_LT01_2_count 0.50430 0.09050 5.573 4.14e-08 ***
## category_code_LT01_4_count 0.64448 0.09375 6.875 1.89e-11 ***
## category_code_LT01_5_count 0.90637 0.06121 14.807 < 2e-16 ***
## category_code_LT01_6_count 0.31521 0.15219 2.071 0.0389 *
## category_code_LT01_12_count -0.02673 0.20346 -0.131 0.8955
## category_code_LT01_14_count 0.11058 0.32297 0.342 0.7322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.639033366522032
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9641 -0.7386 0.0846 0.8934 3.4870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94947 0.08553 116.322 < 2e-16 ***
## category_code_LT01_2_count 0.50661 0.09046 5.600 3.57e-08 ***
## category_code_LT01_4_count 0.64977 0.09294 6.991 8.93e-12 ***
## category_code_LT01_5_count 0.90850 0.06090 14.919 < 2e-16 ***
## category_code_LT01_6_count 0.30981 0.15143 2.046 0.0413 *
## category_code_LT01_12_count -0.02145 0.20291 -0.106 0.9158
## category_code_LT01_15_count -0.03743 0.73379 -0.051 0.9593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.639
## F-statistic: 147.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.639260809688835
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9637 -0.7394 0.0782 0.9012 3.4878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94980 0.08551 116.364 < 2e-16 ***
## category_code_LT01_2_count 0.49933 0.09115 5.478 6.87e-08 ***
## category_code_LT01_4_count 0.65013 0.09263 7.019 7.49e-12 ***
## category_code_LT01_5_count 0.90741 0.06090 14.900 < 2e-16 ***
## category_code_LT01_6_count 0.31723 0.15187 2.089 0.0372 *
## category_code_LT01_12_count -0.02034 0.20277 -0.100 0.9201
## category_code_LT01_16_count 0.64169 1.14849 0.559 0.5766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.63918770988089
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9629 -0.7386 0.0814 0.8848 3.4894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95060 0.08559 116.264 < 2e-16 ***
## category_code_LT01_2_count 0.50132 0.09000 5.570 4.19e-08 ***
## category_code_LT01_4_count 0.64059 0.09409 6.808 2.89e-11 ***
## category_code_LT01_5_count 0.90523 0.06106 14.824 < 2e-16 ***
## category_code_LT01_6_count 0.31419 0.15135 2.076 0.0384 *
## category_code_LT01_13_count 0.07984 0.23788 0.336 0.7373
## category_code_LT01_14_count 0.10784 0.32192 0.335 0.7378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6392
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.639105764131549
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9633 -0.7382 0.0801 0.8849 3.4882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94940 0.08553 116.333 < 2e-16 ***
## category_code_LT01_2_count 0.50377 0.08990 5.604 3.50e-08 ***
## category_code_LT01_4_count 0.64578 0.09324 6.926 1.37e-11 ***
## category_code_LT01_5_count 0.90747 0.06071 14.949 < 2e-16 ***
## category_code_LT01_6_count 0.30914 0.15068 2.052 0.0407 *
## category_code_LT01_13_count 0.07895 0.23840 0.331 0.7406
## category_code_LT01_15_count -0.01961 0.73498 -0.027 0.9787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.639347139865027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9628 -0.7393 0.0707 0.8840 3.4891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94973 0.08550 116.376 < 2e-16 ***
## category_code_LT01_2_count 0.49635 0.09061 5.478 6.88e-08 ***
## category_code_LT01_4_count 0.64613 0.09289 6.956 1.13e-11 ***
## category_code_LT01_5_count 0.90631 0.06071 14.929 < 2e-16 ***
## category_code_LT01_6_count 0.31709 0.15118 2.097 0.0365 *
## category_code_LT01_13_count 0.08503 0.23803 0.357 0.7211
## category_code_LT01_16_count 0.65955 1.14930 0.574 0.5663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.639106931005163
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9633 -0.7388 0.0872 0.8909 3.4889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95064 0.08560 116.249 < 2e-16 ***
## category_code_LT01_2_count 0.50317 0.09001 5.590 3.77e-08 ***
## category_code_LT01_4_count 0.64418 0.09387 6.862 2.05e-11 ***
## category_code_LT01_5_count 0.90571 0.06106 14.832 < 2e-16 ***
## category_code_LT01_6_count 0.31341 0.15146 2.069 0.039 *
## category_code_LT01_14_count 0.10740 0.32197 0.334 0.739
## category_code_LT01_15_count -0.03832 0.73350 -0.052 0.958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.639354064440306
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9629 -0.7407 0.0800 0.8982 3.4899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95112 0.08557 116.292 < 2e-16 ***
## category_code_LT01_2_count 0.49537 0.09078 5.457 7.7e-08 ***
## category_code_LT01_4_count 0.64404 0.09358 6.882 1.8e-11 ***
## category_code_LT01_5_count 0.90435 0.06108 14.806 < 2e-16 ***
## category_code_LT01_6_count 0.32183 0.15203 2.117 0.0348 *
## category_code_LT01_14_count 0.11939 0.32252 0.370 0.7114
## category_code_LT01_16_count 0.67019 1.15073 0.582 0.5606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6394
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.639254102513377
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9633 -0.7393 0.0785 0.9031 3.4885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94978 0.08551 116.359 < 2e-16 ***
## category_code_LT01_2_count 0.49838 0.09062 5.499 6.14e-08 ***
## category_code_LT01_4_count 0.64972 0.09270 7.009 7.96e-12 ***
## category_code_LT01_5_count 0.90687 0.06071 14.939 < 2e-16 ***
## category_code_LT01_6_count 0.31591 0.15126 2.089 0.0373 *
## category_code_LT01_15_count -0.02239 0.73366 -0.031 0.9757
## category_code_LT01_16_count 0.64143 1.14904 0.558 0.5769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.642729615848836
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9759 -0.7300 0.0480 0.8911 3.4737
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95895 0.08505 117.093 < 2e-16 ***
## category_code_LT01_2_count 0.49931 0.08824 5.659 2.60e-08 ***
## category_code_LT01_4_count 0.61802 0.09320 6.631 8.79e-11 ***
## category_code_LT01_5_count 0.91194 0.06093 14.967 < 2e-16 ***
## category_code_LT01_7_count 0.38348 0.15138 2.533 0.0116 *
## category_code_LT01_8_count -0.15617 0.26671 -0.586 0.5585
## category_code_LT01_9_count 0.30998 0.22056 1.405 0.1605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.647, Adjusted R-squared: 0.6427
## F-statistic: 150 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.641737563605503
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9644 -0.7171 0.0288 0.8650 3.3889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94375 0.08831 112.603 < 2e-16 ***
## category_code_LT01_2_count 0.50852 0.08805 5.775 1.36e-08 ***
## category_code_LT01_4_count 0.62221 0.09328 6.670 6.89e-11 ***
## category_code_LT01_5_count 0.91727 0.06088 15.066 < 2e-16 ***
## category_code_LT01_7_count 0.39422 0.15136 2.605 0.00948 **
## category_code_LT01_8_count -0.14864 0.26701 -0.557 0.57798
## category_code_LT01_10_count 0.08569 0.10969 0.781 0.43509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6461, Adjusted R-squared: 0.6417
## F-statistic: 149.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.642938673127944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7262 0.0119 0.8620 3.4623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96707 0.08506 117.173 < 2e-16 ***
## category_code_LT01_2_count 0.46215 0.09431 4.900 1.30e-06 ***
## category_code_LT01_4_count 0.58085 0.09773 5.944 5.29e-09 ***
## category_code_LT01_5_count 0.91495 0.06081 15.047 < 2e-16 ***
## category_code_LT01_7_count 0.35089 0.15461 2.270 0.0237 *
## category_code_LT01_8_count -0.13310 0.26660 -0.499 0.6178
## category_code_LT01_11_count 0.17890 0.11890 1.505 0.1331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6472, Adjusted R-squared: 0.6429
## F-statistic: 150.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.64130406297667
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9825 -0.7092 0.0520 0.8606 3.4652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96201 0.08519 116.934 < 2e-16 ***
## category_code_LT01_2_count 0.51347 0.08867 5.791 1.25e-08 ***
## category_code_LT01_4_count 0.62502 0.09356 6.681 6.47e-11 ***
## category_code_LT01_5_count 0.91693 0.06115 14.996 < 2e-16 ***
## category_code_LT01_7_count 0.40290 0.15105 2.667 0.0079 **
## category_code_LT01_8_count -0.14547 0.26730 -0.544 0.5865
## category_code_LT01_12_count 0.02553 0.20136 0.127 0.8992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6456, Adjusted R-squared: 0.6413
## F-statistic: 149.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.641293739629599
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9830 -0.7145 0.0521 0.8579 3.4643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96208 0.08519 116.934 < 2e-16 ***
## category_code_LT01_2_count 0.51526 0.08774 5.873 7.91e-09 ***
## category_code_LT01_4_count 0.62633 0.09344 6.703 5.61e-11 ***
## category_code_LT01_5_count 0.91767 0.06094 15.058 < 2e-16 ***
## category_code_LT01_7_count 0.40359 0.15208 2.654 0.00822 **
## category_code_LT01_8_count -0.14490 0.26762 -0.541 0.58845
## category_code_LT01_13_count -0.01054 0.23914 -0.044 0.96485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6456, Adjusted R-squared: 0.6413
## F-statistic: 149.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.641293472832809
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9830 -0.7150 0.0494 0.8552 3.4643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96190 0.08529 116.806 < 2e-16 ***
## category_code_LT01_2_count 0.51529 0.08778 5.870 8.02e-09 ***
## category_code_LT01_4_count 0.62649 0.09388 6.673 6.78e-11 ***
## category_code_LT01_5_count 0.91782 0.06119 14.999 < 2e-16 ***
## category_code_LT01_7_count 0.40319 0.15136 2.664 0.00798 **
## category_code_LT01_8_count -0.14404 0.26713 -0.539 0.59000
## category_code_LT01_14_count -0.01271 0.31995 -0.040 0.96832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6456, Adjusted R-squared: 0.6413
## F-statistic: 149.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.641305372927983
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9831 -0.7112 0.0380 0.8571 3.4642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96213 0.08519 116.935 < 2e-16 ***
## category_code_LT01_2_count 0.51430 0.08792 5.850 8.99e-09 ***
## category_code_LT01_4_count 0.62488 0.09361 6.675 6.69e-11 ***
## category_code_LT01_5_count 0.91773 0.06093 15.063 < 2e-16 ***
## category_code_LT01_7_count 0.40353 0.15115 2.670 0.00784 **
## category_code_LT01_8_count -0.14463 0.26713 -0.541 0.58845
## category_code_LT01_15_count 0.09773 0.73112 0.134 0.89372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6456, Adjusted R-squared: 0.6413
## F-statistic: 149.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.641429583026096
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9832 -0.7083 0.0414 0.8596 3.4644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96254 0.08518 116.952 < 2e-16 ***
## category_code_LT01_2_count 0.51052 0.08832 5.780 1.33e-08 ***
## category_code_LT01_4_count 0.62729 0.09324 6.728 4.80e-11 ***
## category_code_LT01_5_count 0.91717 0.06092 15.056 < 2e-16 ***
## category_code_LT01_7_count 0.40382 0.15104 2.674 0.00776 **
## category_code_LT01_8_count -0.15015 0.26742 -0.561 0.57473
## category_code_LT01_16_count 0.49498 1.14171 0.434 0.66481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6414
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.64275993347013
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9562 -0.7100 0.0214 0.8958 3.4206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94291 0.08815 112.790 < 2e-16 ***
## category_code_LT01_2_count 0.49551 0.08853 5.597 3.62e-08 ***
## category_code_LT01_4_count 0.61462 0.09328 6.589 1.15e-10 ***
## category_code_LT01_5_count 0.90661 0.06027 15.043 < 2e-16 ***
## category_code_LT01_7_count 0.37485 0.15161 2.473 0.0138 *
## category_code_LT01_9_count 0.29056 0.22181 1.310 0.1908
## category_code_LT01_10_count 0.06833 0.11019 0.620 0.5355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6471, Adjusted R-squared: 0.6428
## F-statistic: 150 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.644061793352298
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9745 -0.7213 0.0196 0.8987 3.4781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96252 0.08492 117.313 < 2e-16 ***
## category_code_LT01_2_count 0.44857 0.09470 4.737 2.85e-06 ***
## category_code_LT01_4_count 0.57332 0.09766 5.870 8.01e-09 ***
## category_code_LT01_5_count 0.90468 0.06017 15.035 < 2e-16 ***
## category_code_LT01_7_count 0.33078 0.15478 2.137 0.0331 *
## category_code_LT01_9_count 0.29530 0.22015 1.341 0.1804
## category_code_LT01_11_count 0.17538 0.11873 1.477 0.1403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.344 on 491 degrees of freedom
## Multiple R-squared: 0.6484, Adjusted R-squared: 0.6441
## F-statistic: 150.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.642487582225121
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9703 -0.7060 0.0351 0.8965 3.4817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95734 0.08504 117.090 < 2e-16 ***
## category_code_LT01_2_count 0.49868 0.08922 5.589 3.78e-08 ***
## category_code_LT01_4_count 0.61647 0.09357 6.588 1.15e-10 ***
## category_code_LT01_5_count 0.90613 0.06055 14.966 < 2e-16 ***
## category_code_LT01_7_count 0.38086 0.15136 2.516 0.0122 *
## category_code_LT01_9_count 0.30576 0.22052 1.387 0.1662
## category_code_LT01_12_count 0.02031 0.20088 0.101 0.9195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6425
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.64248665728545
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9706 -0.7109 0.0275 0.8948 3.4812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95735 0.08504 117.091 < 2e-16 ***
## category_code_LT01_2_count 0.49965 0.08834 5.656 2.63e-08 ***
## category_code_LT01_4_count 0.61664 0.09347 6.597 1.09e-10 ***
## category_code_LT01_5_count 0.90656 0.06031 15.033 < 2e-16 ***
## category_code_LT01_7_count 0.37907 0.15247 2.486 0.0132 *
## category_code_LT01_9_count 0.30743 0.22115 1.390 0.1651
## category_code_LT01_13_count 0.02260 0.23897 0.095 0.9247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6425
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.642491624960179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9707 -0.7015 0.0328 0.8939 3.4808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95688 0.08513 116.954 < 2e-16 ***
## category_code_LT01_2_count 0.50040 0.08832 5.666 2.50e-08 ***
## category_code_LT01_4_count 0.61866 0.09386 6.592 1.13e-10 ***
## category_code_LT01_5_count 0.90740 0.06055 14.986 < 2e-16 ***
## category_code_LT01_7_count 0.38192 0.15162 2.519 0.0121 *
## category_code_LT01_9_count 0.30742 0.22087 1.392 0.1646
## category_code_LT01_14_count -0.04017 0.31990 -0.126 0.9001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6425
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.642498071985263
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9709 -0.7084 0.0345 0.8959 3.4808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95745 0.08504 117.093 < 2e-16 ***
## category_code_LT01_2_count 0.49899 0.08850 5.638 2.90e-08 ***
## category_code_LT01_4_count 0.61591 0.09364 6.578 1.23e-10 ***
## category_code_LT01_5_count 0.90682 0.06029 15.040 < 2e-16 ***
## category_code_LT01_7_count 0.38159 0.15144 2.520 0.0121 *
## category_code_LT01_9_count 0.30660 0.22057 1.390 0.1651
## category_code_LT01_15_count 0.11456 0.73001 0.157 0.8754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6468, Adjusted R-squared: 0.6425
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.642575202129186
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9708 -0.7021 0.0361 0.9004 3.4812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95776 0.08503 117.102 < 2e-16 ***
## category_code_LT01_2_count 0.49631 0.08884 5.586 3.85e-08 ***
## category_code_LT01_4_count 0.61837 0.09326 6.631 8.82e-11 ***
## category_code_LT01_5_count 0.90622 0.06030 15.029 < 2e-16 ***
## category_code_LT01_7_count 0.38171 0.15136 2.522 0.012 *
## category_code_LT01_9_count 0.30328 0.22061 1.375 0.170
## category_code_LT01_16_count 0.41158 1.13896 0.361 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 491 degrees of freedom
## Multiple R-squared: 0.6469, Adjusted R-squared: 0.6426
## F-statistic: 149.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.643164287218593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9638 -0.7220 0.0094 0.8883 3.3967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94817 0.08818 112.812 < 2e-16 ***
## category_code_LT01_2_count 0.45616 0.09462 4.821 1.91e-06 ***
## category_code_LT01_4_count 0.57637 0.09777 5.895 6.97e-09 ***
## category_code_LT01_5_count 0.90995 0.06010 15.141 < 2e-16 ***
## category_code_LT01_7_count 0.34001 0.15481 2.196 0.0285 *
## category_code_LT01_10_count 0.08190 0.10946 0.748 0.4547
## category_code_LT01_11_count 0.17921 0.11883 1.508 0.1322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.345 on 491 degrees of freedom
## Multiple R-squared: 0.6475, Adjusted R-squared: 0.6432
## F-statistic: 150.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.64151585881455
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9594 -0.7155 0.0473 0.8682 3.3980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94253 0.08831 112.589 < 2e-16 ***
## category_code_LT01_2_count 0.50808 0.08899 5.709 1.97e-08 ***
## category_code_LT01_4_count 0.62086 0.09364 6.630 8.85e-11 ***
## category_code_LT01_5_count 0.91177 0.06048 15.076 < 2e-16 ***
## category_code_LT01_7_count 0.39163 0.15133 2.588 0.00994 **
## category_code_LT01_10_count 0.08406 0.10977 0.766 0.44418
## category_code_LT01_12_count 0.01568 0.20129 0.078 0.93793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6415
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.641511877768447
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9597 -0.7168 0.0434 0.8657 3.3971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94250 0.08831 112.589 < 2e-16 ***
## category_code_LT01_2_count 0.50914 0.08811 5.778 1.34e-08 ***
## category_code_LT01_4_count 0.62163 0.09351 6.647 7.95e-11 ***
## category_code_LT01_5_count 0.91223 0.06023 15.146 < 2e-16 ***
## category_code_LT01_7_count 0.39199 0.15230 2.574 0.0103 *
## category_code_LT01_10_count 0.08443 0.10972 0.769 0.4420
## category_code_LT01_13_count -0.00593 0.23865 -0.025 0.9802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6415
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.641543709708714
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9586 -0.7156 0.0441 0.8662 3.3924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94062 0.08876 111.996 < 2e-16 ***
## category_code_LT01_2_count 0.50952 0.08809 5.784 1.30e-08 ***
## category_code_LT01_4_count 0.62367 0.09388 6.643 8.16e-11 ***
## category_code_LT01_5_count 0.91344 0.06050 15.098 < 2e-16 ***
## category_code_LT01_7_count 0.39314 0.15151 2.595 0.00975 **
## category_code_LT01_10_count 0.08925 0.11212 0.796 0.42639
## category_code_LT01_14_count -0.06874 0.32688 -0.210 0.83353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6415
## F-statistic: 149.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.641514537904015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9599 -0.7161 0.0429 0.8667 3.3976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94266 0.08834 112.552 < 2e-16 ***
## category_code_LT01_2_count 0.50871 0.08825 5.765 1.45e-08 ***
## category_code_LT01_4_count 0.62093 0.09367 6.629 8.94e-11 ***
## category_code_LT01_5_count 0.91227 0.06023 15.147 < 2e-16 ***
## category_code_LT01_7_count 0.39197 0.15146 2.588 0.00994 **
## category_code_LT01_10_count 0.08380 0.11005 0.761 0.44675
## category_code_LT01_15_count 0.04786 0.73320 0.065 0.94798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6458, Adjusted R-squared: 0.6415
## F-statistic: 149.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.641614162021911
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9600 -0.7153 0.0393 0.8757 3.3988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94322 0.08832 112.588 < 2e-16 ***
## category_code_LT01_2_count 0.50522 0.08866 5.699 2.08e-08 ***
## category_code_LT01_4_count 0.62260 0.09333 6.671 6.88e-11 ***
## category_code_LT01_5_count 0.91167 0.06023 15.137 < 2e-16 ***
## category_code_LT01_7_count 0.39250 0.15133 2.594 0.00978 **
## category_code_LT01_10_count 0.08275 0.10977 0.754 0.45130
## category_code_LT01_16_count 0.42799 1.14079 0.375 0.70770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.348 on 491 degrees of freedom
## Multiple R-squared: 0.6459, Adjusted R-squared: 0.6416
## F-statistic: 149.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.64281076367113
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9830 -0.7330 0.0128 0.8639 3.4667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96612 0.08505 117.182 < 2e-16 ***
## category_code_LT01_2_count 0.46333 0.09444 4.906 1.27e-06 ***
## category_code_LT01_4_count 0.57995 0.09772 5.935 5.57e-09 ***
## category_code_LT01_5_count 0.91185 0.06037 15.105 < 2e-16 ***
## category_code_LT01_7_count 0.34546 0.15479 2.232 0.0261 *
## category_code_LT01_11_count 0.18860 0.12255 1.539 0.1244
## category_code_LT01_12_count -0.05605 0.20699 -0.271 0.7867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6471, Adjusted R-squared: 0.6428
## F-statistic: 150.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.642758603081946
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7142 0.0239 0.8648 3.4686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96576 0.08504 117.184 < 2e-16 ***
## category_code_LT01_2_count 0.46216 0.09436 4.898 1.32e-06 ***
## category_code_LT01_4_count 0.57995 0.09790 5.924 5.91e-09 ***
## category_code_LT01_5_count 0.91044 0.06014 15.139 < 2e-16 ***
## category_code_LT01_7_count 0.34859 0.15542 2.243 0.0254 *
## category_code_LT01_11_count 0.18063 0.11891 1.519 0.1294
## category_code_LT01_13_count -0.00958 0.23824 -0.040 0.9679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6471, Adjusted R-squared: 0.6428
## F-statistic: 150 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.642758338777765
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7144 0.0238 0.8649 3.4685
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96561 0.08513 117.057 < 2e-16 ***
## category_code_LT01_2_count 0.46222 0.09443 4.895 1.34e-06 ***
## category_code_LT01_4_count 0.58012 0.09839 5.896 6.94e-09 ***
## category_code_LT01_5_count 0.91060 0.06042 15.072 < 2e-16 ***
## category_code_LT01_7_count 0.34827 0.15485 2.249 0.0249 *
## category_code_LT01_11_count 0.18051 0.11889 1.518 0.1296
## category_code_LT01_14_count -0.01130 0.31928 -0.035 0.9718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6471, Adjusted R-squared: 0.6428
## F-statistic: 150 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.642759007882606
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7142 0.0178 0.8657 3.4686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96576 0.08504 117.184 < 2e-16 ***
## category_code_LT01_2_count 0.46187 0.09444 4.891 1.36e-06 ***
## category_code_LT01_4_count 0.57939 0.09798 5.913 6.30e-09 ***
## category_code_LT01_5_count 0.91044 0.06014 15.139 < 2e-16 ***
## category_code_LT01_7_count 0.34825 0.15469 2.251 0.0248 *
## category_code_LT01_11_count 0.18025 0.11905 1.514 0.1307
## category_code_LT01_15_count 0.03406 0.73060 0.047 0.9628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6471, Adjusted R-squared: 0.6428
## F-statistic: 150 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.642880399548513
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7111 0.0302 0.8697 3.4689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96615 0.08503 117.201 < 2e-16 ***
## category_code_LT01_2_count 0.45768 0.09493 4.821 1.90e-06 ***
## category_code_LT01_4_count 0.58082 0.09775 5.942 5.33e-09 ***
## category_code_LT01_5_count 0.90979 0.06014 15.128 < 2e-16 ***
## category_code_LT01_7_count 0.34872 0.15452 2.257 0.0245 *
## category_code_LT01_11_count 0.18071 0.11887 1.520 0.1291
## category_code_LT01_16_count 0.46789 1.13789 0.411 0.6811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.346 on 491 degrees of freedom
## Multiple R-squared: 0.6472, Adjusted R-squared: 0.6429
## F-statistic: 150.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.641087836014223
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9776 -0.7052 0.0643 0.8644 3.4720
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.960531 0.085175 116.941 < 2e-16 ***
## category_code_LT01_2_count 0.514221 0.088724 5.796 1.22e-08 ***
## category_code_LT01_4_count 0.624490 0.093786 6.659 7.42e-11 ***
## category_code_LT01_5_count 0.912097 0.060521 15.071 < 2e-16 ***
## category_code_LT01_7_count 0.400390 0.152005 2.634 0.0087 **
## category_code_LT01_12_count 0.021408 0.201325 0.106 0.9154
## category_code_LT01_13_count -0.003144 0.238813 -0.013 0.9895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.641089852371237
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9776 -0.7050 0.0630 0.8619 3.4719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96031 0.08527 116.813 < 2e-16 ***
## category_code_LT01_2_count 0.51435 0.08874 5.796 1.21e-08 ***
## category_code_LT01_4_count 0.62499 0.09419 6.635 8.57e-11 ***
## category_code_LT01_5_count 0.91238 0.06076 15.016 < 2e-16 ***
## category_code_LT01_7_count 0.40069 0.15132 2.648 0.00836 **
## category_code_LT01_12_count 0.02210 0.20175 0.110 0.91281
## category_code_LT01_14_count -0.01737 0.32078 -0.054 0.95684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.641099897420372
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9777 -0.7047 0.0583 0.8652 3.4718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96059 0.08517 116.943 < 2e-16 ***
## category_code_LT01_2_count 0.51334 0.08893 5.773 1.38e-08 ***
## category_code_LT01_4_count 0.62326 0.09400 6.630 8.85e-11 ***
## category_code_LT01_5_count 0.91218 0.06052 15.073 < 2e-16 ***
## category_code_LT01_7_count 0.40084 0.15110 2.653 0.00824 **
## category_code_LT01_12_count 0.02195 0.20132 0.109 0.91324
## category_code_LT01_15_count 0.09445 0.73146 0.129 0.89731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.641208646378595
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9776 -0.7005 0.0608 0.8654 3.4723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96092 0.08517 116.958 < 2e-16 ***
## category_code_LT01_2_count 0.50979 0.08933 5.707 1.99e-08 ***
## category_code_LT01_4_count 0.62549 0.09360 6.683 6.39e-11 ***
## category_code_LT01_5_count 0.91145 0.06052 15.060 < 2e-16 ***
## category_code_LT01_7_count 0.40100 0.15100 2.656 0.00817 **
## category_code_LT01_12_count 0.02270 0.20126 0.113 0.91026
## category_code_LT01_16_count 0.46406 1.14070 0.407 0.68432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6455, Adjusted R-squared: 0.6412
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.64108117120007
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7058 0.0583 0.8591 3.4711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.960399 0.085265 116.817 < 2e-16 ***
## category_code_LT01_2_count 0.515787 0.087849 5.871 7.97e-09 ***
## category_code_LT01_4_count 0.625870 0.094130 6.649 7.88e-11 ***
## category_code_LT01_5_count 0.912966 0.060549 15.078 < 2e-16 ***
## category_code_LT01_7_count 0.400756 0.152320 2.631 0.00878 **
## category_code_LT01_13_count -0.002681 0.238766 -0.011 0.99104
## category_code_LT01_14_count -0.014977 0.320039 -0.047 0.96269
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.641091216768004
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9782 -0.7027 0.0546 0.8618 3.4711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9606391 0.0851748 116.943 < 2e-16 ***
## category_code_LT01_2_count 0.5147954 0.0879968 5.850 8.98e-09 ***
## category_code_LT01_4_count 0.6241859 0.0938911 6.648 7.93e-11 ***
## category_code_LT01_5_count 0.9127969 0.0602680 15.146 < 2e-16 ***
## category_code_LT01_7_count 0.4008273 0.1520596 2.636 0.00865 **
## category_code_LT01_13_count -0.0006909 0.2392054 -0.003 0.99770
## category_code_LT01_15_count 0.0924816 0.7326739 0.126 0.89961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.641199370723139
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7003 0.0583 0.8606 3.4715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.960972 0.085166 116.959 < 2e-16 ***
## category_code_LT01_2_count 0.511267 0.088401 5.784 1.30e-08 ***
## category_code_LT01_4_count 0.626375 0.093466 6.702 5.67e-11 ***
## category_code_LT01_5_count 0.912083 0.060272 15.133 < 2e-16 ***
## category_code_LT01_7_count 0.400865 0.151985 2.638 0.00862 **
## category_code_LT01_13_count 0.001117 0.238885 0.005 0.99627
## category_code_LT01_16_count 0.462142 1.141385 0.405 0.68573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6455, Adjusted R-squared: 0.6412
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.641092893465892
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9782 -0.7032 0.0528 0.8598 3.4710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96045 0.08526 116.818 < 2e-16 ***
## category_code_LT01_2_count 0.51497 0.08802 5.851 8.95e-09 ***
## category_code_LT01_4_count 0.62470 0.09430 6.625 9.15e-11 ***
## category_code_LT01_5_count 0.91308 0.06054 15.081 < 2e-16 ***
## category_code_LT01_7_count 0.40124 0.15141 2.650 0.00831 **
## category_code_LT01_14_count -0.01536 0.32003 -0.048 0.96175
## category_code_LT01_15_count 0.09297 0.73131 0.127 0.89889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6454, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.641199805159087
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7006 0.0575 0.8597 3.4715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.960873 0.085259 116.830 < 2e-16 ***
## category_code_LT01_2_count 0.511392 0.088452 5.782 1.32e-08 ***
## category_code_LT01_4_count 0.626681 0.093913 6.673 6.78e-11 ***
## category_code_LT01_5_count 0.912236 0.060554 15.065 < 2e-16 ***
## category_code_LT01_7_count 0.401182 0.151304 2.651 0.00827 **
## category_code_LT01_14_count -0.007955 0.320435 -0.025 0.98020
## category_code_LT01_16_count 0.460406 1.142230 0.403 0.68707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6455, Adjusted R-squared: 0.6412
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.641213922405944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9782 -0.6991 0.0451 0.8632 3.4713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96104 0.08517 116.961 < 2e-16 ***
## category_code_LT01_2_count 0.51035 0.08859 5.761 1.48e-08 ***
## category_code_LT01_4_count 0.62519 0.09365 6.676 6.66e-11 ***
## category_code_LT01_5_count 0.91221 0.06027 15.137 < 2e-16 ***
## category_code_LT01_7_count 0.40170 0.15110 2.659 0.0081 **
## category_code_LT01_15_count 0.10330 0.73161 0.141 0.8878
## category_code_LT01_16_count 0.46768 1.14126 0.410 0.6821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6455, Adjusted R-squared: 0.6412
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.638512580402494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9594 -0.7325 0.0727 0.8735 3.3924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93808 0.08867 112.079 < 2e-16 ***
## category_code_LT01_2_count 0.52196 0.08838 5.906 6.55e-09 ***
## category_code_LT01_4_count 0.67165 0.09104 7.377 6.93e-13 ***
## category_code_LT01_5_count 0.91819 0.06124 14.994 < 2e-16 ***
## category_code_LT01_8_count -0.14002 0.26821 -0.522 0.602
## category_code_LT01_9_count 0.34047 0.22245 1.531 0.127
## category_code_LT01_10_count 0.08674 0.11064 0.784 0.433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 491 degrees of freedom
## Multiple R-squared: 0.6429, Adjusted R-squared: 0.6385
## F-statistic: 147.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.640909722805902
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9819 -0.7529 0.0811 0.8478 3.4669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96339 0.08534 116.755 < 2e-16 ***
## category_code_LT01_2_count 0.45596 0.09506 4.797 2.14e-06 ***
## category_code_LT01_4_count 0.60851 0.09683 6.285 7.26e-10 ***
## category_code_LT01_5_count 0.91407 0.06107 14.967 < 2e-16 ***
## category_code_LT01_8_count -0.12455 0.26733 -0.466 0.641
## category_code_LT01_9_count 0.33785 0.22051 1.532 0.126
## category_code_LT01_11_count 0.22975 0.11639 1.974 0.049 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6452, Adjusted R-squared: 0.6409
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.638069228070832
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9773 -0.7538 0.0652 0.8675 3.4700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95633 0.08560 116.313 < 2e-16 ***
## category_code_LT01_2_count 0.52678 0.08904 5.916 6.18e-09 ***
## category_code_LT01_4_count 0.67527 0.09127 7.399 5.99e-13 ***
## category_code_LT01_5_count 0.91771 0.06150 14.922 < 2e-16 ***
## category_code_LT01_8_count -0.13697 0.26852 -0.510 0.610
## category_code_LT01_9_count 0.36071 0.22107 1.632 0.103
## category_code_LT01_12_count 0.02249 0.20226 0.111 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.6381
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.638159523908854
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9770 -0.7495 0.0613 0.8769 3.4704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95622 0.08559 116.326 < 2e-16 ***
## category_code_LT01_2_count 0.52639 0.08820 5.968 4.60e-09 ***
## category_code_LT01_4_count 0.67257 0.09143 7.356 7.98e-13 ***
## category_code_LT01_5_count 0.91746 0.06131 14.964 < 2e-16 ***
## category_code_LT01_8_count -0.13037 0.26870 -0.485 0.6278
## category_code_LT01_9_count 0.36586 0.22148 1.652 0.0992 .
## category_code_LT01_13_count 0.08779 0.23903 0.367 0.7136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6425, Adjusted R-squared: 0.6382
## F-statistic: 147.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.638060670510382
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9778 -0.7547 0.0619 0.8700 3.4694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.956488 0.085701 116.177 < 2e-16 ***
## category_code_LT01_2_count 0.528128 0.088155 5.991 4.04e-09 ***
## category_code_LT01_4_count 0.675822 0.091743 7.366 7.46e-13 ***
## category_code_LT01_5_count 0.918141 0.061540 14.919 < 2e-16 ***
## category_code_LT01_8_count -0.135933 0.268351 -0.507 0.613
## category_code_LT01_9_count 0.360391 0.221496 1.627 0.104
## category_code_LT01_14_count 0.008798 0.321362 0.027 0.978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.6381
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.638064896815846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9779 -0.7549 0.0614 0.8717 3.4692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95641 0.08560 116.313 < 2e-16 ***
## category_code_LT01_2_count 0.52774 0.08828 5.978 4.35e-09 ***
## category_code_LT01_4_count 0.67551 0.09128 7.401 5.92e-13 ***
## category_code_LT01_5_count 0.91838 0.06128 14.986 < 2e-16 ***
## category_code_LT01_8_count -0.13611 0.26835 -0.507 0.612
## category_code_LT01_9_count 0.36121 0.22114 1.633 0.103
## category_code_LT01_15_count 0.05911 0.73419 0.081 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.6381
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.638147071402774
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9780 -0.7532 0.0613 0.8698 3.4692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95677 0.08560 116.321 < 2e-16 ***
## category_code_LT01_2_count 0.52475 0.08864 5.920 6.06e-09 ***
## category_code_LT01_4_count 0.67735 0.09097 7.446 4.36e-13 ***
## category_code_LT01_5_count 0.91801 0.06127 14.982 < 2e-16 ***
## category_code_LT01_8_count -0.14046 0.26863 -0.523 0.601
## category_code_LT01_9_count 0.35856 0.22114 1.621 0.106
## category_code_LT01_16_count 0.39408 1.14728 0.343 0.731
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6425, Adjusted R-squared: 0.6381
## F-statistic: 147.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.639795615982591
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9682 -0.7375 0.0786 0.8511 3.3690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94565 0.08861 112.241 < 2e-16 ***
## category_code_LT01_2_count 0.46450 0.09499 4.890 1.37e-06 ***
## category_code_LT01_4_count 0.61273 0.09695 6.320 5.87e-10 ***
## category_code_LT01_5_count 0.91994 0.06103 15.075 < 2e-16 ***
## category_code_LT01_8_count -0.11574 0.26765 -0.432 0.6656
## category_code_LT01_10_count 0.09947 0.10975 0.906 0.3652
## category_code_LT01_11_count 0.23582 0.11647 2.025 0.0434 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6441, Adjusted R-squared: 0.6398
## F-statistic: 148.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.636792683714436
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9628 -0.7478 0.0611 0.8515 3.3653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93722 0.08888 111.804 < 2e-16 ***
## category_code_LT01_2_count 0.53838 0.08878 6.064 2.65e-09 ***
## category_code_LT01_4_count 0.68196 0.09131 7.469 3.73e-13 ***
## category_code_LT01_5_count 0.92428 0.06146 15.038 < 2e-16 ***
## category_code_LT01_8_count -0.12754 0.26890 -0.474 0.635
## category_code_LT01_10_count 0.10614 0.11022 0.963 0.336
## category_code_LT01_12_count 0.01624 0.20275 0.080 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 491 degrees of freedom
## Multiple R-squared: 0.6412, Adjusted R-squared: 0.6368
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.636829465321033
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9628 -0.7475 0.0514 0.8500 3.3658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93727 0.08888 111.811 < 2e-16 ***
## category_code_LT01_2_count 0.53847 0.08793 6.124 1.88e-09 ***
## category_code_LT01_4_count 0.68043 0.09142 7.443 4.45e-13 ***
## category_code_LT01_5_count 0.92423 0.06126 15.086 < 2e-16 ***
## category_code_LT01_8_count -0.12303 0.26915 -0.457 0.648
## category_code_LT01_10_count 0.10572 0.11019 0.959 0.338
## category_code_LT01_13_count 0.05665 0.23909 0.237 0.813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 491 degrees of freedom
## Multiple R-squared: 0.6412, Adjusted R-squared: 0.6368
## F-statistic: 146.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.636792329587568
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9626 -0.7491 0.0574 0.8469 3.3627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93648 0.08935 111.207 < 2e-16 ***
## category_code_LT01_2_count 0.53962 0.08789 6.140 1.71e-09 ***
## category_code_LT01_4_count 0.68348 0.09172 7.452 4.18e-13 ***
## category_code_LT01_5_count 0.92516 0.06152 15.037 < 2e-16 ***
## category_code_LT01_8_count -0.12653 0.26872 -0.471 0.638
## category_code_LT01_10_count 0.10828 0.11267 0.961 0.337
## category_code_LT01_14_count -0.02533 0.32865 -0.077 0.939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 491 degrees of freedom
## Multiple R-squared: 0.6412, Adjusted R-squared: 0.6368
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.636788853916725
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9629 -0.7483 0.0589 0.8476 3.3643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93711 0.08891 111.767 < 2e-16 ***
## category_code_LT01_2_count 0.53959 0.08800 6.132 1.79e-09 ***
## category_code_LT01_4_count 0.68285 0.09128 7.481 3.43e-13 ***
## category_code_LT01_5_count 0.92466 0.06125 15.097 < 2e-16 ***
## category_code_LT01_8_count -0.12664 0.26873 -0.471 0.638
## category_code_LT01_10_count 0.10675 0.11048 0.966 0.334
## category_code_LT01_15_count -0.02597 0.73744 -0.035 0.972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 491 degrees of freedom
## Multiple R-squared: 0.6412, Adjusted R-squared: 0.6368
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.636880793638696
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9635 -0.7469 0.0564 0.8552 3.3658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93789 0.08889 111.799 < 2e-16 ***
## category_code_LT01_2_count 0.53580 0.08842 6.059 2.72e-09 ***
## category_code_LT01_4_count 0.68383 0.09103 7.512 2.77e-13 ***
## category_code_LT01_5_count 0.92437 0.06123 15.096 < 2e-16 ***
## category_code_LT01_8_count -0.13152 0.26902 -0.489 0.625
## category_code_LT01_10_count 0.10500 0.11022 0.953 0.341
## category_code_LT01_16_count 0.40735 1.14959 0.354 0.723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 491 degrees of freedom
## Multiple R-squared: 0.6413, Adjusted R-squared: 0.6369
## F-statistic: 146.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.639300669501201
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9916 -0.7565 0.0769 0.8536 3.4539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96742 0.08550 116.572 < 2e-16 ***
## category_code_LT01_2_count 0.47362 0.09480 4.996 8.14e-07 ***
## category_code_LT01_4_count 0.61766 0.09685 6.377 4.17e-10 ***
## category_code_LT01_5_count 0.92231 0.06126 15.056 < 2e-16 ***
## category_code_LT01_8_count -0.10531 0.26802 -0.393 0.6945
## category_code_LT01_11_count 0.25003 0.11991 2.085 0.0376 *
## category_code_LT01_12_count -0.07959 0.20785 -0.383 0.7019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.639215853949845
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9895 -0.7509 0.0782 0.8395 3.4568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96687 0.08551 116.563 < 2e-16 ***
## category_code_LT01_2_count 0.47148 0.09474 4.976 8.98e-07 ***
## category_code_LT01_4_count 0.61638 0.09717 6.343 5.12e-10 ***
## category_code_LT01_5_count 0.92012 0.06110 15.059 < 2e-16 ***
## category_code_LT01_8_count -0.10714 0.26822 -0.399 0.6897
## category_code_LT01_11_count 0.23825 0.11661 2.043 0.0416 *
## category_code_LT01_13_count 0.04209 0.23843 0.177 0.8600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.639202431215854
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7511 0.0800 0.8520 3.4567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96740 0.08560 116.441 < 2e-16 ***
## category_code_LT01_2_count 0.47144 0.09481 4.973 9.15e-07 ***
## category_code_LT01_4_count 0.61633 0.09767 6.310 6.23e-10 ***
## category_code_LT01_5_count 0.91978 0.06136 14.990 < 2e-16 ***
## category_code_LT01_8_count -0.11028 0.26782 -0.412 0.6807
## category_code_LT01_11_count 0.23904 0.11651 2.052 0.0407 *
## category_code_LT01_14_count 0.03636 0.32025 0.114 0.9096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.639194867909142
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9898 -0.7516 0.0796 0.8507 3.4565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96693 0.08551 116.561 < 2e-16 ***
## category_code_LT01_2_count 0.47215 0.09480 4.981 8.80e-07 ***
## category_code_LT01_4_count 0.61807 0.09708 6.367 4.44e-10 ***
## category_code_LT01_5_count 0.92039 0.06109 15.067 < 2e-16 ***
## category_code_LT01_8_count -0.10965 0.26782 -0.409 0.6824
## category_code_LT01_11_count 0.23940 0.11662 2.053 0.0406 *
## category_code_LT01_15_count -0.03741 0.73352 -0.051 0.9593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.639311940259673
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9900 -0.7513 0.0771 0.8509 3.4564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96740 0.08550 116.576 < 2e-16 ***
## category_code_LT01_2_count 0.46764 0.09529 4.907 1.26e-06 ***
## category_code_LT01_4_count 0.61898 0.09690 6.388 3.91e-10 ***
## category_code_LT01_5_count 0.92007 0.06107 15.065 < 2e-16 ***
## category_code_LT01_8_count -0.11532 0.26810 -0.430 0.6673
## category_code_LT01_11_count 0.23939 0.11649 2.055 0.0404 *
## category_code_LT01_16_count 0.46080 1.14496 0.402 0.6875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6437, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.63615720976332
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9853 -0.7493 0.0434 0.8373 3.4594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95976 0.08580 116.083 < 2e-16 ***
## category_code_LT01_2_count 0.54598 0.08849 6.170 1.43e-09 ***
## category_code_LT01_4_count 0.68573 0.09162 7.485 3.34e-13 ***
## category_code_LT01_5_count 0.92422 0.06154 15.018 < 2e-16 ***
## category_code_LT01_8_count -0.11762 0.26952 -0.436 0.663
## category_code_LT01_12_count 0.02187 0.20286 0.108 0.914
## category_code_LT01_13_count 0.06243 0.23929 0.261 0.794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6362
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.636117464729457
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7499 0.0524 0.8356 3.4591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96032 0.08590 115.953 < 2e-16 ***
## category_code_LT01_2_count 0.54656 0.08849 6.177 1.37e-09 ***
## category_code_LT01_4_count 0.68662 0.09199 7.464 3.86e-13 ***
## category_code_LT01_5_count 0.92403 0.06177 14.959 < 2e-16 ***
## category_code_LT01_8_count -0.12213 0.26910 -0.454 0.650
## category_code_LT01_12_count 0.02151 0.20328 0.106 0.916
## category_code_LT01_14_count 0.03872 0.32236 0.120 0.904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.636108081411149
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7494 0.0491 0.8373 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95985 0.08581 116.075 < 2e-16 ***
## category_code_LT01_2_count 0.54675 0.08862 6.169 1.43e-09 ***
## category_code_LT01_4_count 0.68775 0.09153 7.514 2.74e-13 ***
## category_code_LT01_5_count 0.92475 0.06153 15.030 < 2e-16 ***
## category_code_LT01_8_count -0.12187 0.26910 -0.453 0.651
## category_code_LT01_12_count 0.02338 0.20286 0.115 0.908
## category_code_LT01_15_count 0.03094 0.73614 0.042 0.966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.636220550720815
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7498 0.0437 0.8370 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96026 0.08580 116.090 < 2e-16 ***
## category_code_LT01_2_count 0.54278 0.08905 6.095 2.21e-09 ***
## category_code_LT01_4_count 0.68931 0.09121 7.557 2.03e-13 ***
## category_code_LT01_5_count 0.92430 0.06152 15.025 < 2e-16 ***
## category_code_LT01_8_count -0.12719 0.26940 -0.472 0.637
## category_code_LT01_12_count 0.02465 0.20281 0.122 0.903
## category_code_LT01_16_count 0.45067 1.15003 0.392 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6406, Adjusted R-squared: 0.6362
## F-statistic: 145.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.636160822996974
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7499 0.0426 0.8356 3.4591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96032 0.08589 115.963 < 2e-16 ***
## category_code_LT01_2_count 0.54679 0.08764 6.239 9.53e-10 ***
## category_code_LT01_4_count 0.68492 0.09218 7.430 4.84e-13 ***
## category_code_LT01_5_count 0.92402 0.06161 14.998 < 2e-16 ***
## category_code_LT01_8_count -0.11699 0.26934 -0.434 0.664
## category_code_LT01_13_count 0.06316 0.23922 0.264 0.792
## category_code_LT01_14_count 0.04130 0.32159 0.128 0.898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6406, Adjusted R-squared: 0.6362
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.636150968466915
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7494 0.0359 0.8449 3.4587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95983 0.08580 116.082 < 2e-16 ***
## category_code_LT01_2_count 0.54705 0.08774 6.235 9.74e-10 ***
## category_code_LT01_4_count 0.68608 0.09168 7.483 3.38e-13 ***
## category_code_LT01_5_count 0.92484 0.06133 15.080 < 2e-16 ***
## category_code_LT01_8_count -0.11660 0.26933 -0.433 0.665
## category_code_LT01_13_count 0.06395 0.23972 0.267 0.790
## category_code_LT01_15_count 0.04170 0.73744 0.057 0.955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6362
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.636266899628077
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7498 0.0350 0.8454 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96024 0.08579 116.098 < 2e-16 ***
## category_code_LT01_2_count 0.54312 0.08816 6.161 1.51e-09 ***
## category_code_LT01_4_count 0.68774 0.09132 7.531 2.43e-13 ***
## category_code_LT01_5_count 0.92437 0.06132 15.075 < 2e-16 ***
## category_code_LT01_8_count -0.12174 0.26960 -0.452 0.652
## category_code_LT01_13_count 0.06656 0.23934 0.278 0.781
## category_code_LT01_16_count 0.45977 1.15053 0.400 0.690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6407, Adjusted R-squared: 0.6363
## F-statistic: 145.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.636110253668536
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9862 -0.7500 0.0488 0.8352 3.4584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96041 0.08590 115.955 < 2e-16 ***
## category_code_LT01_2_count 0.54770 0.08774 6.242 9.33e-10 ***
## category_code_LT01_4_count 0.68708 0.09205 7.464 3.84e-13 ***
## category_code_LT01_5_count 0.92460 0.06159 15.012 < 2e-16 ***
## category_code_LT01_8_count -0.12119 0.26894 -0.451 0.652
## category_code_LT01_14_count 0.04096 0.32162 0.127 0.899
## category_code_LT01_15_count 0.02822 0.73596 0.038 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.636226188524494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7505 0.0429 0.8361 3.4585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96092 0.08589 115.967 < 2e-16 ***
## category_code_LT01_2_count 0.54362 0.08820 6.164 1.48e-09 ***
## category_code_LT01_4_count 0.68840 0.09173 7.504 2.93e-13 ***
## category_code_LT01_5_count 0.92404 0.06158 15.005 < 2e-16 ***
## category_code_LT01_8_count -0.12663 0.26924 -0.470 0.638
## category_code_LT01_14_count 0.04818 0.32206 0.150 0.881
## category_code_LT01_16_count 0.45769 1.15162 0.397 0.691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6406, Adjusted R-squared: 0.6362
## F-statistic: 145.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.636211717762802
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9864 -0.7499 0.0325 0.8425 3.4580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96033 0.08580 116.088 < 2e-16 ***
## category_code_LT01_2_count 0.54408 0.08826 6.164 1.48e-09 ***
## category_code_LT01_4_count 0.68987 0.09120 7.564 1.93e-13 ***
## category_code_LT01_5_count 0.92500 0.06130 15.090 < 2e-16 ***
## category_code_LT01_8_count -0.12611 0.26923 -0.468 0.640
## category_code_LT01_15_count 0.03927 0.73630 0.053 0.957
## category_code_LT01_16_count 0.45030 1.15059 0.391 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6406, Adjusted R-squared: 0.6362
## F-statistic: 145.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.641137338709107
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9606 -0.7347 0.0459 0.8739 3.4019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94519 0.08842 112.476 < 2e-16 ***
## category_code_LT01_2_count 0.45086 0.09529 4.732 2.92e-06 ***
## category_code_LT01_4_count 0.60368 0.09688 6.231 9.98e-10 ***
## category_code_LT01_5_count 0.90964 0.06037 15.068 < 2e-16 ***
## category_code_LT01_9_count 0.31549 0.22179 1.422 0.1555
## category_code_LT01_10_count 0.08016 0.11026 0.727 0.4675
## category_code_LT01_11_count 0.22879 0.11636 1.966 0.0498 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.349 on 491 degrees of freedom
## Multiple R-squared: 0.6455, Adjusted R-squared: 0.6411
## F-statistic: 149 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.638314954967476
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9548 -0.7305 0.0667 0.8720 3.4007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93697 0.08867 112.069 < 2e-16 ***
## category_code_LT01_2_count 0.52166 0.08931 5.841 9.47e-09 ***
## category_code_LT01_4_count 0.67019 0.09139 7.334 9.32e-13 ***
## category_code_LT01_5_count 0.91309 0.06083 15.009 < 2e-16 ***
## category_code_LT01_9_count 0.33672 0.22239 1.514 0.131
## category_code_LT01_10_count 0.08534 0.11073 0.771 0.441
## category_code_LT01_12_count 0.01292 0.20218 0.064 0.949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6427, Adjusted R-squared: 0.6383
## F-statistic: 147.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.638411938169673
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9547 -0.7298 0.0529 0.8751 3.4021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93714 0.08866 112.084 < 2e-16 ***
## category_code_LT01_2_count 0.52071 0.08851 5.883 7.47e-09 ***
## category_code_LT01_4_count 0.66721 0.09152 7.290 1.25e-12 ***
## category_code_LT01_5_count 0.91280 0.06059 15.064 < 2e-16 ***
## category_code_LT01_9_count 0.34230 0.22288 1.536 0.125
## category_code_LT01_10_count 0.08419 0.11070 0.760 0.447
## category_code_LT01_13_count 0.08796 0.23872 0.368 0.713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 491 degrees of freedom
## Multiple R-squared: 0.6428, Adjusted R-squared: 0.6384
## F-statistic: 147.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.638326518427615
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9543 -0.7311 0.0627 0.8691 3.3968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93566 0.08913 111.469 < 2e-16 ***
## category_code_LT01_2_count 0.52280 0.08842 5.912 6.32e-09 ***
## category_code_LT01_4_count 0.67229 0.09175 7.327 9.73e-13 ***
## category_code_LT01_5_count 0.91427 0.06086 15.023 < 2e-16 ***
## category_code_LT01_9_count 0.33792 0.22256 1.518 0.130
## category_code_LT01_10_count 0.08885 0.11305 0.786 0.432
## category_code_LT01_14_count -0.04616 0.32819 -0.141 0.888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 491 degrees of freedom
## Multiple R-squared: 0.6427, Adjusted R-squared: 0.6383
## F-statistic: 147.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.638312047799896
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9550 -0.7309 0.0663 0.8719 3.4001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93697 0.08870 112.032 < 2e-16 ***
## category_code_LT01_2_count 0.52241 0.08857 5.898 6.84e-09 ***
## category_code_LT01_4_count 0.67060 0.09137 7.339 8.97e-13 ***
## category_code_LT01_5_count 0.91346 0.06058 15.078 < 2e-16 ***
## category_code_LT01_9_count 0.33680 0.22252 1.514 0.131
## category_code_LT01_10_count 0.08549 0.11102 0.770 0.442
## category_code_LT01_15_count 0.00859 0.73629 0.012 0.991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6427, Adjusted R-squared: 0.6383
## F-statistic: 147.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.638374279134677
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9553 -0.7307 0.0481 0.8721 3.4011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93748 0.08868 112.060 < 2e-16 ***
## category_code_LT01_2_count 0.51962 0.08893 5.843 9.35e-09 ***
## category_code_LT01_4_count 0.67173 0.09111 7.373 7.16e-13 ***
## category_code_LT01_5_count 0.91308 0.06058 15.071 < 2e-16 ***
## category_code_LT01_9_count 0.33500 0.22245 1.506 0.133
## category_code_LT01_10_count 0.08446 0.11071 0.763 0.446
## category_code_LT01_16_count 0.33344 1.14618 0.291 0.771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.354 on 491 degrees of freedom
## Multiple R-squared: 0.6427, Adjusted R-squared: 0.6384
## F-statistic: 147.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.640860883833306
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9797 -0.7523 0.0768 0.8380 3.4700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96273 0.08531 116.777 < 2e-16 ***
## category_code_LT01_2_count 0.45772 0.09517 4.809 2.02e-06 ***
## category_code_LT01_4_count 0.60724 0.09680 6.273 7.76e-10 ***
## category_code_LT01_5_count 0.91188 0.06062 15.042 < 2e-16 ***
## category_code_LT01_9_count 0.33337 0.22039 1.513 0.131
## category_code_LT01_11_count 0.24188 0.11977 2.020 0.044 *
## category_code_LT01_12_count -0.08032 0.20720 -0.388 0.698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6452, Adjusted R-squared: 0.6409
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.640817711971504
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9772 -0.7516 0.0666 0.8458 3.4735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96206 0.08531 116.775 < 2e-16 ***
## category_code_LT01_2_count 0.45498 0.09513 4.783 2.29e-06 ***
## category_code_LT01_4_count 0.60485 0.09713 6.227 1.02e-09 ***
## category_code_LT01_5_count 0.90933 0.06042 15.050 < 2e-16 ***
## category_code_LT01_9_count 0.33862 0.22089 1.533 0.1259
## category_code_LT01_11_count 0.22923 0.11651 1.967 0.0497 *
## category_code_LT01_13_count 0.07190 0.23805 0.302 0.7627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6452, Adjusted R-squared: 0.6408
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.640751107094108
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9777 -0.7518 0.0782 0.8484 3.4728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.962250 0.085415 116.634 < 2e-16 ***
## category_code_LT01_2_count 0.455928 0.095151 4.792 2.20e-06 ***
## category_code_LT01_4_count 0.607093 0.097570 6.222 1.05e-09 ***
## category_code_LT01_5_count 0.909752 0.060679 14.993 < 2e-16 ***
## category_code_LT01_9_count 0.333963 0.220838 1.512 0.1311
## category_code_LT01_11_count 0.230903 0.116391 1.984 0.0478 *
## category_code_LT01_14_count 0.004408 0.320141 0.014 0.9890
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6408
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.640751139199601
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9777 -0.7518 0.0780 0.8481 3.4728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96219 0.08532 116.767 < 2e-16 ***
## category_code_LT01_2_count 0.45605 0.09518 4.791 2.20e-06 ***
## category_code_LT01_4_count 0.60736 0.09703 6.259 8.44e-10 ***
## category_code_LT01_5_count 0.90982 0.06041 15.060 < 2e-16 ***
## category_code_LT01_9_count 0.33406 0.22049 1.515 0.130
## category_code_LT01_11_count 0.23099 0.11651 1.983 0.048 *
## category_code_LT01_15_count -0.01119 0.73215 -0.015 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6451, Adjusted R-squared: 0.6408
## F-statistic: 148.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.640832958471817
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9778 -0.7514 0.0802 0.8558 3.4729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96255 0.08531 116.776 < 2e-16 ***
## category_code_LT01_2_count 0.45251 0.09563 4.732 2.91e-06 ***
## category_code_LT01_4_count 0.60830 0.09685 6.281 7.42e-10 ***
## category_code_LT01_5_count 0.90940 0.06041 15.054 < 2e-16 ***
## category_code_LT01_9_count 0.33185 0.22050 1.505 0.1330
## category_code_LT01_11_count 0.23122 0.11638 1.987 0.0475 *
## category_code_LT01_16_count 0.38220 1.14161 0.335 0.7379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 491 degrees of freedom
## Multiple R-squared: 0.6452, Adjusted R-squared: 0.6408
## F-statistic: 148.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.63799109908593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9723 -0.7444 0.0620 0.8732 3.4771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95488 0.08557 116.338 < 2e-16 ***
## category_code_LT01_2_count 0.52558 0.08916 5.895 6.99e-09 ***
## category_code_LT01_4_count 0.67072 0.09175 7.310 1.09e-12 ***
## category_code_LT01_5_count 0.91253 0.06088 14.988 < 2e-16 ***
## category_code_LT01_9_count 0.36240 0.22142 1.637 0.102
## category_code_LT01_12_count 0.01675 0.20219 0.083 0.934
## category_code_LT01_13_count 0.09376 0.23878 0.393 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.638
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.637877582250907
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9728 -0.7459 0.0596 0.8647 3.4764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.955048 0.085683 116.184 < 2e-16 ***
## category_code_LT01_2_count 0.527413 0.089101 5.919 6.08e-09 ***
## category_code_LT01_4_count 0.674258 0.092034 7.326 9.79e-13 ***
## category_code_LT01_5_count 0.913098 0.061114 14.941 < 2e-16 ***
## category_code_LT01_9_count 0.356589 0.221424 1.610 0.108
## category_code_LT01_12_count 0.018366 0.202650 0.091 0.928
## category_code_LT01_14_count 0.004702 0.322180 0.015 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6422, Adjusted R-squared: 0.6379
## F-statistic: 146.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.63788170929488
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.7482 0.0611 0.8650 3.4762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95502 0.08558 116.322 < 2e-16 ***
## category_code_LT01_2_count 0.52697 0.08928 5.902 6.69e-09 ***
## category_code_LT01_4_count 0.67380 0.09165 7.352 8.25e-13 ***
## category_code_LT01_5_count 0.91324 0.06088 15.002 < 2e-16 ***
## category_code_LT01_9_count 0.35720 0.22106 1.616 0.107
## category_code_LT01_12_count 0.01892 0.20222 0.094 0.925
## category_code_LT01_15_count 0.05598 0.73451 0.076 0.939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6423, Adjusted R-squared: 0.6379
## F-statistic: 146.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.637952557136352
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9729 -0.7449 0.0524 0.8699 3.4765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95531 0.08558 116.329 < 2e-16 ***
## category_code_LT01_2_count 0.52416 0.08964 5.848 9.11e-09 ***
## category_code_LT01_4_count 0.67547 0.09132 7.396 6.10e-13 ***
## category_code_LT01_5_count 0.91274 0.06088 14.992 < 2e-16 ***
## category_code_LT01_9_count 0.35461 0.22108 1.604 0.109
## category_code_LT01_12_count 0.01964 0.20218 0.097 0.923
## category_code_LT01_16_count 0.36591 1.14630 0.319 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6423, Adjusted R-squared: 0.638
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.637986356474241
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9726 -0.7446 0.0606 0.8723 3.4766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.955009 0.085667 116.205 < 2e-16 ***
## category_code_LT01_2_count 0.526576 0.088305 5.963 4.73e-09 ***
## category_code_LT01_4_count 0.671110 0.092240 7.276 1.37e-12 ***
## category_code_LT01_5_count 0.912873 0.060907 14.988 < 2e-16 ***
## category_code_LT01_9_count 0.362206 0.221850 1.633 0.103
## category_code_LT01_13_count 0.094209 0.238716 0.395 0.693
## category_code_LT01_14_count 0.006684 0.321368 0.021 0.983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.638
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.637993530070938
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9727 -0.7463 0.0599 0.8722 3.4765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95496 0.08557 116.340 < 2e-16 ***
## category_code_LT01_2_count 0.52600 0.08846 5.946 5.21e-09 ***
## category_code_LT01_4_count 0.67048 0.09184 7.301 1.16e-12 ***
## category_code_LT01_5_count 0.91307 0.06063 15.059 < 2e-16 ***
## category_code_LT01_9_count 0.36314 0.22151 1.639 0.102
## category_code_LT01_13_count 0.09583 0.23925 0.401 0.689
## category_code_LT01_15_count 0.07418 0.73586 0.101 0.920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.638
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.638067625086484
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9727 -0.7448 0.0543 0.8736 3.4767
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95526 0.08556 116.349 < 2e-16 ***
## category_code_LT01_2_count 0.52323 0.08881 5.892 7.10e-09 ***
## category_code_LT01_4_count 0.67237 0.09145 7.352 8.23e-13 ***
## category_code_LT01_5_count 0.91255 0.06064 15.049 < 2e-16 ***
## category_code_LT01_9_count 0.36041 0.22149 1.627 0.104
## category_code_LT01_13_count 0.09718 0.23886 0.407 0.684
## category_code_LT01_16_count 0.38152 1.14677 0.333 0.740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6424, Adjusted R-squared: 0.6381
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.637875551655949
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9733 -0.7497 0.0584 0.8730 3.4757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.955144 0.085681 116.189 < 2e-16 ***
## category_code_LT01_2_count 0.528126 0.088383 5.975 4.41e-09 ***
## category_code_LT01_4_count 0.674322 0.092099 7.322 1.01e-12 ***
## category_code_LT01_5_count 0.913642 0.060898 15.003 < 2e-16 ***
## category_code_LT01_9_count 0.356985 0.221495 1.612 0.108
## category_code_LT01_14_count 0.006482 0.321432 0.020 0.984
## category_code_LT01_15_count 0.054267 0.734355 0.074 0.941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6422, Adjusted R-squared: 0.6379
## F-statistic: 146.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.637946722558446
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9733 -0.7465 0.0512 0.8738 3.4759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95552 0.08568 116.193 < 2e-16 ***
## category_code_LT01_2_count 0.52528 0.08877 5.917 6.14e-09 ***
## category_code_LT01_4_count 0.67577 0.09178 7.363 7.64e-13 ***
## category_code_LT01_5_count 0.91306 0.06091 14.991 < 2e-16 ***
## category_code_LT01_9_count 0.35415 0.22153 1.599 0.111
## category_code_LT01_14_count 0.01256 0.32191 0.039 0.969
## category_code_LT01_16_count 0.36661 1.14801 0.319 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6423, Adjusted R-squared: 0.6379
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.637950938876022
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9734 -0.7471 0.0517 0.8744 3.4757
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95540 0.08558 116.330 < 2e-16 ***
## category_code_LT01_2_count 0.52490 0.08889 5.905 6.59e-09 ***
## category_code_LT01_4_count 0.67556 0.09132 7.397 6.06e-13 ***
## category_code_LT01_5_count 0.91336 0.06063 15.066 < 2e-16 ***
## category_code_LT01_9_count 0.35514 0.22114 1.606 0.109
## category_code_LT01_15_count 0.06251 0.73468 0.085 0.932
## category_code_LT01_16_count 0.36742 1.14683 0.320 0.749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 491 degrees of freedom
## Multiple R-squared: 0.6423, Adjusted R-squared: 0.638
## F-statistic: 147 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.639792742258227
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9663 -0.7346 0.0461 0.8515 3.3712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94503 0.08859 112.261 < 2e-16 ***
## category_code_LT01_2_count 0.46622 0.09508 4.904 1.28e-06 ***
## category_code_LT01_4_count 0.61142 0.09691 6.309 6.26e-10 ***
## category_code_LT01_5_count 0.91817 0.06055 15.165 < 2e-16 ***
## category_code_LT01_10_count 0.09972 0.10977 0.908 0.3641
## category_code_LT01_11_count 0.24891 0.11981 2.078 0.0383 *
## category_code_LT01_12_count -0.08883 0.20760 -0.428 0.6689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6441, Adjusted R-squared: 0.6398
## F-statistic: 148.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.639681434764669
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9644 -0.7344 0.0559 0.8526 3.3763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94480 0.08860 112.244 < 2e-16 ***
## category_code_LT01_2_count 0.46402 0.09503 4.883 1.42e-06 ***
## category_code_LT01_4_count 0.61022 0.09721 6.278 7.57e-10 ***
## category_code_LT01_5_count 0.91570 0.06034 15.174 < 2e-16 ***
## category_code_LT01_10_count 0.09783 0.10977 0.891 0.3733
## category_code_LT01_11_count 0.23595 0.11657 2.024 0.0435 *
## category_code_LT01_13_count 0.04214 0.23797 0.177 0.8595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.6397
## F-statistic: 148.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.639663467230707
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9641 -0.7336 0.0708 0.8530 3.3736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94399 0.08907 111.644 < 2e-16 ***
## category_code_LT01_2_count 0.46466 0.09504 4.889 1.37e-06 ***
## category_code_LT01_4_count 0.61250 0.09762 6.275 7.71e-10 ***
## category_code_LT01_5_count 0.91644 0.06063 15.115 < 2e-16 ***
## category_code_LT01_10_count 0.10027 0.11225 0.893 0.3721
## category_code_LT01_11_count 0.23685 0.11647 2.034 0.0425 *
## category_code_LT01_14_count -0.02713 0.32733 -0.083 0.9340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.6397
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.639669845901133
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9641 -0.7402 0.0839 0.8527 3.3748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94449 0.08863 112.208 < 2e-16 ***
## category_code_LT01_2_count 0.46488 0.09507 4.890 1.37e-06 ***
## category_code_LT01_4_count 0.61228 0.09711 6.305 6.42e-10 ***
## category_code_LT01_5_count 0.91579 0.06034 15.177 < 2e-16 ***
## category_code_LT01_10_count 0.09936 0.11006 0.903 0.3671
## category_code_LT01_11_count 0.23745 0.11656 2.037 0.0422 *
## category_code_LT01_15_count -0.09170 0.73511 -0.125 0.9008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.644, Adjusted R-squared: 0.6397
## F-statistic: 148 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.639746748363361
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9648 -0.7350 0.0748 0.8528 3.3770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.94540 0.08861 112.236 < 2e-16 ***
## category_code_LT01_2_count 0.46084 0.09556 4.823 1.89e-06 ***
## category_code_LT01_4_count 0.61262 0.09696 6.318 5.95e-10 ***
## category_code_LT01_5_count 0.91546 0.06034 15.171 < 2e-16 ***
## category_code_LT01_10_count 0.09685 0.10981 0.882 0.3782
## category_code_LT01_11_count 0.23715 0.11646 2.036 0.0422 *
## category_code_LT01_16_count 0.39679 1.14364 0.347 0.7288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 491 degrees of freedom
## Multiple R-squared: 0.6441, Adjusted R-squared: 0.6397
## F-statistic: 148.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.636677308675815
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9586 -0.7653 0.0494 0.8535 3.3734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93631 0.08887 111.809 < 2e-16 ***
## category_code_LT01_2_count 0.53792 0.08886 6.053 2.82e-09 ***
## category_code_LT01_4_count 0.67881 0.09173 7.400 5.94e-13 ***
## category_code_LT01_5_count 0.91964 0.06082 15.122 < 2e-16 ***
## category_code_LT01_10_count 0.10422 0.11025 0.945 0.345
## category_code_LT01_12_count 0.01151 0.20269 0.057 0.955
## category_code_LT01_13_count 0.06271 0.23879 0.263 0.793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6367
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.636631734140407
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9583 -0.7677 0.0660 0.8523 3.3704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93540 0.08934 111.206 < 2e-16 ***
## category_code_LT01_2_count 0.53907 0.08881 6.070 2.56e-09 ***
## category_code_LT01_4_count 0.68208 0.09200 7.414 5.40e-13 ***
## category_code_LT01_5_count 0.92048 0.06107 15.072 < 2e-16 ***
## category_code_LT01_10_count 0.10698 0.11270 0.949 0.343
## category_code_LT01_12_count 0.01376 0.20305 0.068 0.946
## category_code_LT01_14_count -0.02830 0.32935 -0.086 0.932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6366
## F-statistic: 146.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.636627355370118
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9586 -0.7668 0.0669 0.8529 3.3721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93610 0.08890 111.764 < 2e-16 ***
## category_code_LT01_2_count 0.53911 0.08896 6.060 2.71e-09 ***
## category_code_LT01_4_count 0.68141 0.09163 7.436 4.65e-13 ***
## category_code_LT01_5_count 0.91995 0.06081 15.128 < 2e-16 ***
## category_code_LT01_10_count 0.10530 0.11056 0.952 0.341
## category_code_LT01_12_count 0.01246 0.20272 0.061 0.951
## category_code_LT01_15_count -0.02826 0.73782 -0.038 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6366
## F-statistic: 146.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.636707485233588
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9590 -0.7639 0.0595 0.8534 3.3738
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93681 0.08888 111.796 < 2e-16 ***
## category_code_LT01_2_count 0.53547 0.08939 5.991 4.05e-09 ***
## category_code_LT01_4_count 0.68220 0.09136 7.467 3.77e-13 ***
## category_code_LT01_5_count 0.91949 0.06082 15.119 < 2e-16 ***
## category_code_LT01_10_count 0.10354 0.11029 0.939 0.348
## category_code_LT01_12_count 0.01386 0.20266 0.068 0.945
## category_code_LT01_16_count 0.38054 1.14860 0.331 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6367
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.636679577873262
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9584 -0.7672 0.0478 0.8525 3.3710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93557 0.08934 111.213 < 2e-16 ***
## category_code_LT01_2_count 0.53886 0.08799 6.124 1.87e-09 ***
## category_code_LT01_4_count 0.68018 0.09216 7.381 6.78e-13 ***
## category_code_LT01_5_count 0.92043 0.06086 15.123 < 2e-16 ***
## category_code_LT01_10_count 0.10633 0.11271 0.943 0.346
## category_code_LT01_13_count 0.06283 0.23874 0.263 0.793
## category_code_LT01_14_count -0.02607 0.32870 -0.079 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6367
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.636675284419776
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9588 -0.7662 0.0484 0.8530 3.3727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93624 0.08890 111.771 < 2e-16 ***
## category_code_LT01_2_count 0.53876 0.08812 6.114 1.98e-09 ***
## category_code_LT01_4_count 0.67943 0.09176 7.404 5.78e-13 ***
## category_code_LT01_5_count 0.91993 0.06057 15.188 < 2e-16 ***
## category_code_LT01_10_count 0.10464 0.11053 0.947 0.344
## category_code_LT01_13_count 0.06265 0.23928 0.262 0.794
## category_code_LT01_15_count -0.01634 0.73921 -0.022 0.982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6367
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.636760993986996
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9591 -0.7634 0.0467 0.8535 3.3744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93694 0.08888 111.804 < 2e-16 ***
## category_code_LT01_2_count 0.53513 0.08854 6.044 2.98e-09 ***
## category_code_LT01_4_count 0.68029 0.09145 7.439 4.56e-13 ***
## category_code_LT01_5_count 0.91946 0.06057 15.180 < 2e-16 ***
## category_code_LT01_10_count 0.10296 0.11025 0.934 0.351
## category_code_LT01_13_count 0.06630 0.23890 0.278 0.782
## category_code_LT01_16_count 0.39201 1.14927 0.341 0.733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.357 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6368
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.636629556829983
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9584 -0.7687 0.0645 0.8519 3.3695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93532 0.08937 111.167 < 2e-16 ***
## category_code_LT01_2_count 0.54014 0.08806 6.134 1.77e-09 ***
## category_code_LT01_4_count 0.68287 0.09203 7.420 5.19e-13 ***
## category_code_LT01_5_count 0.92079 0.06086 15.130 < 2e-16 ***
## category_code_LT01_10_count 0.10751 0.11301 0.951 0.342
## category_code_LT01_14_count -0.02702 0.32872 -0.082 0.935
## category_code_LT01_15_count -0.02994 0.73759 -0.041 0.968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.641, Adjusted R-squared: 0.6366
## F-statistic: 146.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.636706752631745
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9590 -0.7657 0.0588 0.8527 3.3717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93621 0.08937 111.184 < 2e-16 ***
## category_code_LT01_2_count 0.53656 0.08850 6.063 2.67e-09 ***
## category_code_LT01_4_count 0.68343 0.09175 7.449 4.26e-13 ***
## category_code_LT01_5_count 0.92025 0.06087 15.119 < 2e-16 ***
## category_code_LT01_10_count 0.10528 0.11281 0.933 0.351
## category_code_LT01_14_count -0.02000 0.32936 -0.061 0.952
## category_code_LT01_16_count 0.37464 1.15080 0.326 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6367
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.636704575187735
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9592 -0.7650 0.0585 0.8530 3.3730
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.93671 0.08891 111.757 < 2e-16 ***
## category_code_LT01_2_count 0.53651 0.08861 6.055 2.80e-09 ***
## category_code_LT01_4_count 0.68293 0.09132 7.478 3.49e-13 ***
## category_code_LT01_5_count 0.91985 0.06056 15.188 < 2e-16 ***
## category_code_LT01_10_count 0.10406 0.11056 0.941 0.347
## category_code_LT01_15_count -0.02014 0.73803 -0.027 0.978
## category_code_LT01_16_count 0.37794 1.14926 0.329 0.742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 491 degrees of freedom
## Multiple R-squared: 0.6411, Adjusted R-squared: 0.6367
## F-statistic: 146.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.639217986168715
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9879 -0.7544 0.0771 0.8658 3.4591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96635 0.08548 116.599 < 2e-16 ***
## category_code_LT01_2_count 0.47304 0.09484 4.988 8.49e-07 ***
## category_code_LT01_4_count 0.61496 0.09712 6.332 5.46e-10 ***
## category_code_LT01_5_count 0.91848 0.06060 15.155 < 2e-16 ***
## category_code_LT01_11_count 0.25043 0.11997 2.087 0.0374 *
## category_code_LT01_12_count -0.08371 0.20768 -0.403 0.6871
## category_code_LT01_13_count 0.04869 0.23806 0.205 0.8380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.639200715924966
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9881 -0.7548 0.0795 0.8515 3.4591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96695 0.08557 116.473 < 2e-16 ***
## category_code_LT01_2_count 0.47301 0.09490 4.985 8.63e-07 ***
## category_code_LT01_4_count 0.61481 0.09764 6.297 6.74e-10 ***
## category_code_LT01_5_count 0.91797 0.06086 15.083 < 2e-16 ***
## category_code_LT01_11_count 0.25157 0.11988 2.098 0.0364 *
## category_code_LT01_12_count -0.08512 0.20817 -0.409 0.6828
## category_code_LT01_14_count 0.04346 0.32099 0.135 0.8924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.639190841571756
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9880 -0.7545 0.0768 0.8582 3.4589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96638 0.08548 116.596 < 2e-16 ***
## category_code_LT01_2_count 0.47387 0.09491 4.993 8.28e-07 ***
## category_code_LT01_4_count 0.61696 0.09704 6.358 4.68e-10 ***
## category_code_LT01_5_count 0.91868 0.06060 15.160 < 2e-16 ***
## category_code_LT01_11_count 0.25184 0.12003 2.098 0.0364 *
## category_code_LT01_12_count -0.08367 0.20780 -0.403 0.6874
## category_code_LT01_15_count -0.05134 0.73390 -0.070 0.9443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6392
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.639290512510075
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9881 -0.7547 0.0787 0.8636 3.4591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96676 0.08547 116.609 < 2e-16 ***
## category_code_LT01_2_count 0.46953 0.09541 4.921 1.17e-06 ***
## category_code_LT01_4_count 0.61760 0.09686 6.377 4.18e-10 ***
## category_code_LT01_5_count 0.91818 0.06060 15.151 < 2e-16 ***
## category_code_LT01_11_count 0.25153 0.11986 2.098 0.0364 *
## category_code_LT01_12_count -0.08198 0.20767 -0.395 0.6932
## category_code_LT01_16_count 0.42878 1.14364 0.375 0.7079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6393
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.639107218391037
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7464 0.0801 0.8495 3.4623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96623 0.08557 116.470 < 2e-16 ***
## category_code_LT01_2_count 0.47081 0.09486 4.963 9.57e-07 ***
## category_code_LT01_4_count 0.61369 0.09794 6.266 8.12e-10 ***
## category_code_LT01_5_count 0.91572 0.06069 15.088 < 2e-16 ***
## category_code_LT01_11_count 0.23896 0.11662 2.049 0.041 *
## category_code_LT01_13_count 0.04759 0.23808 0.200 0.842
## category_code_LT01_14_count 0.03464 0.32026 0.108 0.914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.639099968811094
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7515 0.0799 0.8446 3.4621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96579 0.08548 116.588 < 2e-16 ***
## category_code_LT01_2_count 0.47147 0.09486 4.970 9.25e-07 ***
## category_code_LT01_4_count 0.61534 0.09738 6.319 5.91e-10 ***
## category_code_LT01_5_count 0.91633 0.06040 15.172 < 2e-16 ***
## category_code_LT01_11_count 0.23928 0.11674 2.050 0.0409 *
## category_code_LT01_13_count 0.04680 0.23863 0.196 0.8446
## category_code_LT01_15_count -0.03149 0.73523 -0.043 0.9659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.639209853373704
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7462 0.0793 0.8457 3.4623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96618 0.08547 116.603 < 2e-16 ***
## category_code_LT01_2_count 0.46708 0.09536 4.898 1.31e-06 ***
## category_code_LT01_4_count 0.61606 0.09715 6.341 5.18e-10 ***
## category_code_LT01_5_count 0.91579 0.06040 15.163 < 2e-16 ***
## category_code_LT01_11_count 0.23926 0.11660 2.052 0.0407 *
## category_code_LT01_13_count 0.05111 0.23823 0.215 0.8302
## category_code_LT01_16_count 0.44530 1.14451 0.389 0.6974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6436, Adjusted R-squared: 0.6392
## F-statistic: 147.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.63908023527466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9860 -0.7528 0.0822 0.8567 3.4620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96627 0.08557 116.467 < 2e-16 ***
## category_code_LT01_2_count 0.47157 0.09491 4.968 9.34e-07 ***
## category_code_LT01_4_count 0.61557 0.09785 6.291 6.99e-10 ***
## category_code_LT01_5_count 0.91593 0.06069 15.092 < 2e-16 ***
## category_code_LT01_11_count 0.24028 0.11662 2.060 0.0399 *
## category_code_LT01_14_count 0.03452 0.32028 0.108 0.9142
## category_code_LT01_15_count -0.04182 0.73360 -0.057 0.9546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6434, Adjusted R-squared: 0.6391
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.639188151417894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7464 0.0822 0.8575 3.4622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96675 0.08557 116.479 < 2e-16 ***
## category_code_LT01_2_count 0.46709 0.09544 4.894 1.34e-06 ***
## category_code_LT01_4_count 0.61607 0.09766 6.308 6.30e-10 ***
## category_code_LT01_5_count 0.91531 0.06070 15.080 < 2e-16 ***
## category_code_LT01_11_count 0.24026 0.11649 2.062 0.0397 *
## category_code_LT01_14_count 0.04118 0.32070 0.128 0.8979
## category_code_LT01_16_count 0.44376 1.14539 0.387 0.6986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6392
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.639177388805481
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7524 0.0813 0.8560 3.4619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96622 0.08547 116.598 < 2e-16 ***
## category_code_LT01_2_count 0.46793 0.09542 4.904 1.28e-06 ***
## category_code_LT01_4_count 0.61794 0.09708 6.365 4.47e-10 ***
## category_code_LT01_5_count 0.91605 0.06039 15.168 < 2e-16 ***
## category_code_LT01_11_count 0.24057 0.11660 2.063 0.0396 *
## category_code_LT01_15_count -0.03149 0.73393 -0.043 0.9658
## category_code_LT01_16_count 0.43399 1.14438 0.379 0.7047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.353 on 491 degrees of freedom
## Multiple R-squared: 0.6435, Adjusted R-squared: 0.6392
## F-statistic: 147.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.636026067516974
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9813 -0.7486 0.0463 0.8372 3.4653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95904 0.08587 115.984 < 2e-16 ***
## category_code_LT01_2_count 0.54584 0.08858 6.162 1.50e-09 ***
## category_code_LT01_4_count 0.68322 0.09243 7.392 6.29e-13 ***
## category_code_LT01_5_count 0.91955 0.06114 15.041 < 2e-16 ***
## category_code_LT01_12_count 0.01675 0.20322 0.082 0.934
## category_code_LT01_13_count 0.06868 0.23893 0.287 0.774
## category_code_LT01_14_count 0.03741 0.32238 0.116 0.908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.636
## F-statistic: 145.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.636018330319754
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9813 -0.7481 0.0395 0.8388 3.4649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95859 0.08577 116.105 < 2e-16 ***
## category_code_LT01_2_count 0.54592 0.08874 6.152 1.59e-09 ***
## category_code_LT01_4_count 0.68418 0.09202 7.435 4.69e-13 ***
## category_code_LT01_5_count 0.92026 0.06087 15.118 < 2e-16 ***
## category_code_LT01_12_count 0.01862 0.20279 0.092 0.927
## category_code_LT01_13_count 0.06938 0.23943 0.290 0.772
## category_code_LT01_15_count 0.04059 0.73772 0.055 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.636
## F-statistic: 145.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.63612273895724
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9812 -0.7485 0.0391 0.8410 3.4653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95893 0.08576 116.119 < 2e-16 ***
## category_code_LT01_2_count 0.54215 0.08916 6.081 2.41e-09 ***
## category_code_LT01_4_count 0.68567 0.09163 7.483 3.38e-13 ***
## category_code_LT01_5_count 0.91962 0.06088 15.106 < 2e-16 ***
## category_code_LT01_12_count 0.01957 0.20273 0.097 0.923
## category_code_LT01_13_count 0.07206 0.23907 0.301 0.763
## category_code_LT01_16_count 0.43608 1.14952 0.379 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.63596574185318
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7486 0.0441 0.8368 3.4648
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95908 0.08587 115.974 < 2e-16 ***
## category_code_LT01_2_count 0.54677 0.08871 6.164 1.48e-09 ***
## category_code_LT01_4_count 0.68553 0.09235 7.424 5.07e-13 ***
## category_code_LT01_5_count 0.91998 0.06113 15.049 < 2e-16 ***
## category_code_LT01_12_count 0.01826 0.20324 0.090 0.928
## category_code_LT01_14_count 0.03680 0.32242 0.114 0.909
## category_code_LT01_15_count 0.02592 0.73626 0.035 0.972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.636
## F-statistic: 145.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.636068820518852
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7491 0.0439 0.8377 3.4651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95950 0.08587 115.985 < 2e-16 ***
## category_code_LT01_2_count 0.54289 0.08916 6.089 2.30e-09 ***
## category_code_LT01_4_count 0.68670 0.09201 7.463 3.88e-13 ***
## category_code_LT01_5_count 0.91927 0.06114 15.034 < 2e-16 ***
## category_code_LT01_12_count 0.01906 0.20317 0.094 0.925
## category_code_LT01_14_count 0.04342 0.32283 0.134 0.893
## category_code_LT01_16_count 0.43091 1.15040 0.375 0.708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.636057223534096
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7485 0.0354 0.8464 3.4647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95898 0.08577 116.108 < 2e-16 ***
## category_code_LT01_2_count 0.54315 0.08930 6.083 2.38e-09 ***
## category_code_LT01_4_count 0.68793 0.09156 7.513 2.75e-13 ***
## category_code_LT01_5_count 0.92009 0.06087 15.115 < 2e-16 ***
## category_code_LT01_12_count 0.02115 0.20276 0.104 0.917
## category_code_LT01_15_count 0.03640 0.73660 0.049 0.961
## category_code_LT01_16_count 0.42461 1.14948 0.369 0.712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.636023043858934
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7487 0.0397 0.8368 3.4647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95912 0.08586 115.988 < 2e-16 ***
## category_code_LT01_2_count 0.54657 0.08787 6.220 1.06e-09 ***
## category_code_LT01_4_count 0.68336 0.09255 7.384 6.64e-13 ***
## category_code_LT01_5_count 0.92003 0.06092 15.101 < 2e-16 ***
## category_code_LT01_13_count 0.06994 0.23938 0.292 0.770
## category_code_LT01_14_count 0.03911 0.32163 0.122 0.903
## category_code_LT01_15_count 0.03843 0.73757 0.052 0.958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6404, Adjusted R-squared: 0.636
## F-statistic: 145.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.636130992275629
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9816 -0.7478 0.0397 0.8378 3.4651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95955 0.08586 116.000 < 2e-16 ***
## category_code_LT01_2_count 0.54267 0.08833 6.144 1.67e-09 ***
## category_code_LT01_4_count 0.68461 0.09217 7.428 4.93e-13 ***
## category_code_LT01_5_count 0.91928 0.06093 15.087 < 2e-16 ***
## category_code_LT01_13_count 0.07277 0.23902 0.304 0.761
## category_code_LT01_14_count 0.04606 0.32206 0.143 0.886
## category_code_LT01_16_count 0.44341 1.15111 0.385 0.700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.636119198541267
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9817 -0.7486 0.0301 0.8487 3.4646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95901 0.08576 116.120 < 2e-16 ***
## category_code_LT01_2_count 0.54298 0.08842 6.141 1.69e-09 ***
## category_code_LT01_4_count 0.68586 0.09168 7.481 3.43e-13 ***
## category_code_LT01_5_count 0.92023 0.06062 15.179 < 2e-16 ***
## category_code_LT01_13_count 0.07364 0.23955 0.307 0.759
## category_code_LT01_15_count 0.04974 0.73798 0.067 0.946
## category_code_LT01_16_count 0.43729 1.15021 0.380 0.704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.636063875435392
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9820 -0.7492 0.0357 0.8373 3.4644
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95960 0.08587 115.989 < 2e-16 ***
## category_code_LT01_2_count 0.54382 0.08843 6.150 1.61e-09 ***
## category_code_LT01_4_count 0.68702 0.09206 7.463 3.88e-13 ***
## category_code_LT01_5_count 0.91980 0.06093 15.097 < 2e-16 ***
## category_code_LT01_14_count 0.04539 0.32209 0.141 0.888
## category_code_LT01_15_count 0.03395 0.73640 0.046 0.963
## category_code_LT01_16_count 0.43139 1.15101 0.375 0.708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.359 on 491 degrees of freedom
## Multiple R-squared: 0.6405, Adjusted R-squared: 0.6361
## F-statistic: 145.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.61893379069934
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0198 -0.7917 0.0061 0.9802 3.8251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97590 0.08784 113.570 < 2e-16 ***
## category_code_LT01_2_count 0.76804 0.07723 9.944 < 2e-16 ***
## category_code_LT01_5_count 0.95080 0.06262 15.183 < 2e-16 ***
## category_code_LT01_6_count 0.49345 0.15179 3.251 0.00123 **
## category_code_LT01_7_count 0.61526 0.15160 4.059 5.75e-05 ***
## category_code_LT01_8_count -0.17066 0.27568 -0.619 0.53616
## category_code_LT01_9_count 0.36021 0.22768 1.582 0.11427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.617252516637863
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7888 -0.0296 0.9806 3.8352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96574 0.09119 109.282 < 2e-16 ***
## category_code_LT01_2_count 0.78551 0.07650 10.267 < 2e-16 ***
## category_code_LT01_5_count 0.95787 0.06261 15.299 < 2e-16 ***
## category_code_LT01_6_count 0.49219 0.15383 3.200 0.00147 **
## category_code_LT01_7_count 0.63373 0.15158 4.181 3.44e-05 ***
## category_code_LT01_8_count -0.15981 0.27619 -0.579 0.56312
## category_code_LT01_10_count 0.06639 0.11465 0.579 0.56283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.623334965737512
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0286 -0.7387 -0.0040 0.9214 3.8141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98690 0.08734 114.349 < 2e-16 ***
## category_code_LT01_2_count 0.65271 0.08930 7.309 1.1e-12 ***
## category_code_LT01_5_count 0.94752 0.06221 15.231 < 2e-16 ***
## category_code_LT01_6_count 0.43083 0.15296 2.817 0.00505 **
## category_code_LT01_7_count 0.51024 0.15660 3.258 0.00120 **
## category_code_LT01_8_count -0.13405 0.27407 -0.489 0.62500
## category_code_LT01_11_count 0.33920 0.11795 2.876 0.00421 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6233
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.617067074862144
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7845 -0.0282 0.9775 3.8214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97951 0.08802 113.373 < 2e-16 ***
## category_code_LT01_2_count 0.78468 0.07769 10.100 < 2e-16 ***
## category_code_LT01_5_count 0.95599 0.06286 15.208 < 2e-16 ***
## category_code_LT01_6_count 0.50086 0.15291 3.275 0.00113 **
## category_code_LT01_7_count 0.64043 0.15110 4.238 2.69e-05 ***
## category_code_LT01_8_count -0.16032 0.27639 -0.580 0.56214
## category_code_LT01_12_count 0.06508 0.20859 0.312 0.75518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.61712801884097
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7808 -0.0219 0.9750 3.8217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97926 0.08802 113.375 < 2e-16 ***
## category_code_LT01_2_count 0.78637 0.07656 10.271 < 2e-16 ***
## category_code_LT01_5_count 0.95665 0.06267 15.265 < 2e-16 ***
## category_code_LT01_6_count 0.50637 0.15194 3.333 0.000925 ***
## category_code_LT01_7_count 0.63231 0.15252 4.146 3.99e-05 ***
## category_code_LT01_8_count -0.15035 0.27670 -0.543 0.587135
## category_code_LT01_13_count 0.10325 0.24646 0.419 0.675444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.617759958764175
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0259 -0.7809 -0.0042 0.9697 3.8184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98257 0.08799 113.446 < 2e-16 ***
## category_code_LT01_2_count 0.77579 0.07738 10.026 < 2e-16 ***
## category_code_LT01_5_count 0.94995 0.06305 15.067 < 2e-16 ***
## category_code_LT01_6_count 0.51759 0.15224 3.400 0.000729 ***
## category_code_LT01_7_count 0.62620 0.15169 4.128 4.3e-05 ***
## category_code_LT01_8_count -0.16227 0.27602 -0.588 0.556859
## category_code_LT01_14_count 0.32678 0.32884 0.994 0.320832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.617221730238982
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.7820 -0.0341 0.9737 3.8212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97976 0.08801 113.399 < 2e-16 ***
## category_code_LT01_2_count 0.78389 0.07688 10.196 < 2e-16 ***
## category_code_LT01_5_count 0.95799 0.06262 15.300 < 2e-16 ***
## category_code_LT01_6_count 0.50148 0.15217 3.296 0.00105 **
## category_code_LT01_7_count 0.64224 0.15107 4.251 2.54e-05 ***
## category_code_LT01_8_count -0.15911 0.27619 -0.576 0.56482
## category_code_LT01_15_count 0.40963 0.75322 0.544 0.58680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.617229250140975
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0281 -0.7817 -0.0226 0.9723 3.8209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98002 0.08801 113.399 < 2e-16 ***
## category_code_LT01_2_count 0.78239 0.07725 10.128 < 2e-16 ***
## category_code_LT01_5_count 0.95684 0.06263 15.277 < 2e-16 ***
## category_code_LT01_6_count 0.51460 0.15267 3.371 0.000809 ***
## category_code_LT01_7_count 0.64278 0.15108 4.254 2.51e-05 ***
## category_code_LT01_8_count -0.16585 0.27659 -0.600 0.549047
## category_code_LT01_16_count 0.65483 1.18491 0.553 0.580761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.618764622493593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0049 -0.7861 0.0202 0.9710 3.8363
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96463 0.09098 109.521 < 2e-16 ***
## category_code_LT01_2_count 0.76707 0.07740 9.910 < 2e-16 ***
## category_code_LT01_5_count 0.94548 0.06199 15.253 < 2e-16 ***
## category_code_LT01_6_count 0.47989 0.15350 3.126 0.00188 **
## category_code_LT01_7_count 0.60766 0.15195 3.999 7.34e-05 ***
## category_code_LT01_9_count 0.34595 0.22893 1.511 0.13139
## category_code_LT01_10_count 0.04677 0.11507 0.406 0.68459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6188
## F-statistic: 135.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.624776325477615
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0160 -0.7823 0.0454 0.9318 3.8190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98200 0.08717 114.515 < 2e-16 ***
## category_code_LT01_2_count 0.63613 0.08985 7.080 5.02e-12 ***
## category_code_LT01_5_count 0.93650 0.06157 15.211 < 2e-16 ***
## category_code_LT01_6_count 0.41677 0.15265 2.730 0.00656 **
## category_code_LT01_7_count 0.48575 0.15682 3.097 0.00206 **
## category_code_LT01_9_count 0.32956 0.22601 1.458 0.14544
## category_code_LT01_11_count 0.33384 0.11778 2.835 0.00478 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.618700853229065
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0131 -0.8038 0.0041 0.9824 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97420 0.08783 113.565 < 2e-16 ***
## category_code_LT01_2_count 0.76490 0.07864 9.726 < 2e-16 ***
## category_code_LT01_5_count 0.94355 0.06225 15.158 < 2e-16 ***
## category_code_LT01_6_count 0.48446 0.15269 3.173 0.0016 **
## category_code_LT01_7_count 0.61151 0.15158 4.034 6.35e-05 ***
## category_code_LT01_9_count 0.35571 0.22764 1.563 0.1188
## category_code_LT01_12_count 0.05995 0.20802 0.288 0.7733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6233, Adjusted R-squared: 0.6187
## F-statistic: 135.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.61888505462214
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0130 -0.7898 0.0031 0.9888 3.8271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97384 0.08781 113.584 < 2e-16 ***
## category_code_LT01_2_count 0.76457 0.07764 9.847 < 2e-16 ***
## category_code_LT01_5_count 0.94393 0.06201 15.222 < 2e-16 ***
## category_code_LT01_6_count 0.48956 0.15166 3.228 0.001330 **
## category_code_LT01_7_count 0.59984 0.15308 3.919 0.000102 ***
## category_code_LT01_9_count 0.36507 0.22816 1.600 0.110226
## category_code_LT01_13_count 0.13929 0.24607 0.566 0.571619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619238290249563
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0124 -0.7801 0.0195 0.9556 3.8239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97704 0.08782 113.606 < 2e-16 ***
## category_code_LT01_2_count 0.75799 0.07822 9.691 < 2e-16 ***
## category_code_LT01_5_count 0.93848 0.06241 15.037 < 2e-16 ***
## category_code_LT01_6_count 0.49992 0.15206 3.288 0.00108 **
## category_code_LT01_7_count 0.59986 0.15210 3.944 9.18e-05 ***
## category_code_LT01_9_count 0.34226 0.22801 1.501 0.13397
## category_code_LT01_14_count 0.28978 0.32891 0.881 0.37873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.618886769671221
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7872 -0.0016 0.9903 3.8265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97443 0.08781 113.595 < 2e-16 ***
## category_code_LT01_2_count 0.76335 0.07789 9.800 < 2e-16 ***
## category_code_LT01_5_count 0.94541 0.06197 15.255 < 2e-16 ***
## category_code_LT01_6_count 0.48438 0.15193 3.188 0.00152 **
## category_code_LT01_7_count 0.61315 0.15153 4.046 6.04e-05 ***
## category_code_LT01_9_count 0.35835 0.22763 1.574 0.11606
## category_code_LT01_15_count 0.42694 0.75166 0.568 0.57030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.61880462981984
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0141 -0.7887 0.0096 0.9844 3.8263
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97463 0.08782 113.582 < 2e-16 ***
## category_code_LT01_2_count 0.76359 0.07815 9.771 < 2e-16 ***
## category_code_LT01_5_count 0.94433 0.06201 15.229 < 2e-16 ***
## category_code_LT01_6_count 0.49645 0.15242 3.257 0.0012 **
## category_code_LT01_7_count 0.61371 0.15158 4.049 5.98e-05 ***
## category_code_LT01_9_count 0.35211 0.22776 1.546 0.1227
## category_code_LT01_16_count 0.55002 1.18139 0.466 0.6417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6234, Adjusted R-squared: 0.6188
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.62339564449662
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0108 -0.7360 0.0221 0.9129 3.8287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97224 0.09047 110.227 < 2e-16 ***
## category_code_LT01_2_count 0.64912 0.08952 7.251 1.62e-12 ***
## category_code_LT01_5_count 0.94311 0.06152 15.330 < 2e-16 ***
## category_code_LT01_6_count 0.41364 0.15465 2.675 0.00773 **
## category_code_LT01_7_count 0.49984 0.15694 3.185 0.00154 **
## category_code_LT01_10_count 0.06416 0.11371 0.564 0.57286
## category_code_LT01_11_count 0.34066 0.11789 2.890 0.00403 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.617052131724247
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0085 -0.7867 -0.0174 0.9898 3.8365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96447 0.09119 109.274 < 2e-16 ***
## category_code_LT01_2_count 0.78228 0.07792 10.040 < 2e-16 ***
## category_code_LT01_5_count 0.95094 0.06221 15.285 < 2e-16 ***
## category_code_LT01_6_count 0.48383 0.15466 3.128 0.00186 **
## category_code_LT01_7_count 0.63009 0.15154 4.158 3.79e-05 ***
## category_code_LT01_10_count 0.06461 0.11470 0.563 0.57350
## category_code_LT01_12_count 0.05812 0.20853 0.279 0.78057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.617142174274184
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0089 -0.7760 -0.0113 0.9955 3.8366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96438 0.09118 109.286 < 2e-16 ***
## category_code_LT01_2_count 0.78330 0.07682 10.196 < 2e-16 ***
## category_code_LT01_5_count 0.95170 0.06197 15.358 < 2e-16 ***
## category_code_LT01_6_count 0.48911 0.15373 3.182 0.00156 **
## category_code_LT01_7_count 0.62173 0.15286 4.067 5.54e-05 ***
## category_code_LT01_10_count 0.06420 0.11468 0.560 0.57586
## category_code_LT01_13_count 0.10816 0.24607 0.440 0.66047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.61759011868547
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0121 -0.7830 0.0121 0.9618 3.8290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97195 0.09153 108.950 < 2e-16 ***
## category_code_LT01_2_count 0.77549 0.07746 10.011 < 2e-16 ***
## category_code_LT01_5_count 0.94536 0.06244 15.139 < 2e-16 ***
## category_code_LT01_6_count 0.50367 0.15460 3.258 0.0012 **
## category_code_LT01_7_count 0.61968 0.15194 4.079 5.29e-05 ***
## category_code_LT01_10_count 0.04200 0.11765 0.357 0.7213
## category_code_LT01_14_count 0.29600 0.33764 0.877 0.3811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.61718247177478
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0107 -0.7757 -0.0256 0.9841 3.8355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96546 0.09120 109.274 < 2e-16 ***
## category_code_LT01_2_count 0.78167 0.07709 10.140 < 2e-16 ***
## category_code_LT01_5_count 0.95276 0.06194 15.382 < 2e-16 ***
## category_code_LT01_6_count 0.48508 0.15387 3.153 0.00172 **
## category_code_LT01_7_count 0.63215 0.15153 4.172 3.57e-05 ***
## category_code_LT01_10_count 0.06101 0.11499 0.531 0.59597
## category_code_LT01_15_count 0.37383 0.75543 0.495 0.62092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.617178683532799
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0098 -0.7838 -0.0130 0.9873 3.8356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96535 0.09119 109.278 < 2e-16 ***
## category_code_LT01_2_count 0.78044 0.07746 10.076 < 2e-16 ***
## category_code_LT01_5_count 0.95154 0.06197 15.355 < 2e-16 ***
## category_code_LT01_6_count 0.49641 0.15455 3.212 0.00141 **
## category_code_LT01_7_count 0.63234 0.15154 4.173 3.56e-05 ***
## category_code_LT01_10_count 0.06232 0.11482 0.543 0.58752
## category_code_LT01_16_count 0.58051 1.18489 0.490 0.62440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.62328675114868
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0261 -0.7537 0.0021 0.9342 3.8149
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98607 0.08731 114.374 < 2e-16 ***
## category_code_LT01_2_count 0.65411 0.08937 7.319 1.02e-12 ***
## category_code_LT01_5_count 0.94513 0.06173 15.310 < 2e-16 ***
## category_code_LT01_6_count 0.43193 0.15321 2.819 0.00501 **
## category_code_LT01_7_count 0.50314 0.15672 3.210 0.00141 **
## category_code_LT01_11_count 0.35329 0.12155 2.907 0.00382 **
## category_code_LT01_12_count -0.08950 0.21314 -0.420 0.67473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6233
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.623239135908494
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0235 -0.7341 0.0076 0.9371 3.8156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98540 0.08731 114.363 < 2e-16 ***
## category_code_LT01_2_count 0.65096 0.08946 7.277 1.37e-12 ***
## category_code_LT01_5_count 0.94241 0.06156 15.310 < 2e-16 ***
## category_code_LT01_6_count 0.42782 0.15281 2.800 0.00532 **
## category_code_LT01_7_count 0.50063 0.15757 3.177 0.00158 **
## category_code_LT01_11_count 0.33927 0.11802 2.875 0.00422 **
## category_code_LT01_13_count 0.08256 0.24424 0.338 0.73549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.623727710861848
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0220 -0.7776 0.0196 0.9098 3.8129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98809 0.08730 114.413 < 2e-16 ***
## category_code_LT01_2_count 0.64278 0.08999 7.143 3.31e-12 ***
## category_code_LT01_5_count 0.93629 0.06198 15.107 < 2e-16 ***
## category_code_LT01_6_count 0.43792 0.15320 2.858 0.00444 **
## category_code_LT01_7_count 0.49562 0.15691 3.159 0.00168 **
## category_code_LT01_11_count 0.33649 0.11795 2.853 0.00452 **
## category_code_LT01_14_count 0.28313 0.32651 0.867 0.38628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.623239966866313
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0244 -0.7348 0.0063 0.9114 3.8153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98568 0.08731 114.370 < 2e-16 ***
## category_code_LT01_2_count 0.65047 0.08956 7.263 1.49e-12 ***
## category_code_LT01_5_count 0.94324 0.06154 15.328 < 2e-16 ***
## category_code_LT01_6_count 0.42485 0.15296 2.778 0.00569 **
## category_code_LT01_7_count 0.50868 0.15656 3.249 0.00124 **
## category_code_LT01_11_count 0.33809 0.11821 2.860 0.00442 **
## category_code_LT01_15_count 0.25440 0.74907 0.340 0.73429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.623352937516688
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0238 -0.7344 0.0103 0.9158 3.8150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98596 0.08730 114.388 < 2e-16 ***
## category_code_LT01_2_count 0.64647 0.09013 7.173 2.72e-12 ***
## category_code_LT01_5_count 0.94198 0.06155 15.303 < 2e-16 ***
## category_code_LT01_6_count 0.43476 0.15349 2.833 0.00481 **
## category_code_LT01_7_count 0.50840 0.15647 3.249 0.00124 **
## category_code_LT01_11_count 0.34066 0.11790 2.889 0.00403 **
## category_code_LT01_16_count 0.60145 1.17358 0.512 0.60854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616958417580531
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0211 -0.7915 -0.0082 0.9824 3.8233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97769 0.08800 113.385 < 2e-16 ***
## category_code_LT01_2_count 0.78281 0.07797 10.040 < 2e-16 ***
## category_code_LT01_5_count 0.95000 0.06224 15.262 < 2e-16 ***
## category_code_LT01_6_count 0.49788 0.15282 3.258 0.0012 **
## category_code_LT01_7_count 0.62815 0.15241 4.121 4.42e-05 ***
## category_code_LT01_12_count 0.05814 0.20859 0.279 0.7806
## category_code_LT01_13_count 0.10929 0.24617 0.444 0.6573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617524068858625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0198 -0.7883 0.0082 0.9707 3.8201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98084 0.08798 113.442 < 2e-16 ***
## category_code_LT01_2_count 0.77394 0.07859 9.847 < 2e-16 ***
## category_code_LT01_5_count 0.94359 0.06262 15.068 < 2e-16 ***
## category_code_LT01_6_count 0.50971 0.15324 3.326 0.000947 ***
## category_code_LT01_7_count 0.62291 0.15165 4.108 4.68e-05 ***
## category_code_LT01_12_count 0.04317 0.20915 0.206 0.836547
## category_code_LT01_14_count 0.31730 0.33017 0.961 0.337023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617034956773974
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0221 -0.7786 -0.0247 0.9780 3.8228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97811 0.08799 113.404 < 2e-16 ***
## category_code_LT01_2_count 0.78019 0.07835 9.957 < 2e-16 ***
## category_code_LT01_5_count 0.95095 0.06221 15.285 < 2e-16 ***
## category_code_LT01_6_count 0.49232 0.15307 3.216 0.00138 **
## category_code_LT01_7_count 0.63836 0.15102 4.227 2.83e-05 ***
## category_code_LT01_12_count 0.06333 0.20852 0.304 0.76146
## category_code_LT01_15_count 0.40945 0.75353 0.543 0.58712
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.617
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617017452075255
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0214 -0.7944 -0.0057 0.9792 3.8227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97829 0.08799 113.401 < 2e-16 ***
## category_code_LT01_2_count 0.77922 0.07869 9.903 < 2e-16 ***
## category_code_LT01_5_count 0.94968 0.06225 15.256 < 2e-16 ***
## category_code_LT01_6_count 0.50495 0.15350 3.290 0.00108 **
## category_code_LT01_7_count 0.63870 0.15104 4.229 2.8e-05 ***
## category_code_LT01_12_count 0.06177 0.20848 0.296 0.76713
## category_code_LT01_16_count 0.61812 1.18344 0.522 0.60169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617652784060352
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0197 -0.7756 0.0176 0.9722 3.8203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98068 0.08797 113.460 < 2e-16 ***
## category_code_LT01_2_count 0.77348 0.07771 9.954 < 2e-16 ***
## category_code_LT01_5_count 0.94375 0.06244 15.115 < 2e-16 ***
## category_code_LT01_6_count 0.51389 0.15211 3.378 0.000787 ***
## category_code_LT01_7_count 0.61372 0.15301 4.011 6.99e-05 ***
## category_code_LT01_13_count 0.11210 0.24584 0.456 0.648607
## category_code_LT01_14_count 0.32378 0.32883 0.985 0.325284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.617144674282625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7770 -0.0090 0.9815 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97792 0.08798 113.416 < 2e-16 ***
## category_code_LT01_2_count 0.78104 0.07726 10.109 < 2e-16 ***
## category_code_LT01_5_count 0.95178 0.06197 15.359 < 2e-16 ***
## category_code_LT01_6_count 0.49781 0.15204 3.274 0.00113 **
## category_code_LT01_7_count 0.62915 0.15237 4.129 4.28e-05 ***
## category_code_LT01_13_count 0.11892 0.24638 0.483 0.62953
## category_code_LT01_15_count 0.42447 0.75438 0.563 0.57391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617124212562859
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0218 -0.7893 -0.0061 0.9822 3.8229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97810 0.08798 113.413 < 2e-16 ***
## category_code_LT01_2_count 0.78001 0.07762 10.050 < 2e-16 ***
## category_code_LT01_5_count 0.95044 0.06201 15.328 < 2e-16 ***
## category_code_LT01_6_count 0.51072 0.15251 3.349 0.000874 ***
## category_code_LT01_7_count 0.62966 0.15238 4.132 4.23e-05 ***
## category_code_LT01_13_count 0.11672 0.24622 0.474 0.635673
## category_code_LT01_16_count 0.63810 1.18422 0.539 0.590244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.617695198587369
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0210 -0.7899 0.0049 0.9727 3.8199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98107 0.08796 113.473 < 2e-16 ***
## category_code_LT01_2_count 0.77182 0.07797 9.899 < 2e-16 ***
## category_code_LT01_5_count 0.94497 0.06241 15.142 < 2e-16 ***
## category_code_LT01_6_count 0.50886 0.15237 3.340 0.000902 ***
## category_code_LT01_7_count 0.62437 0.15163 4.118 4.49e-05 ***
## category_code_LT01_14_count 0.31896 0.32892 0.970 0.332660
## category_code_LT01_15_count 0.38571 0.75295 0.512 0.608697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617757768802048
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0201 -0.7884 0.0155 0.9648 3.8195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98144 0.08796 113.482 < 2e-16 ***
## category_code_LT01_2_count 0.76893 0.07851 9.794 < 2e-16 ***
## category_code_LT01_5_count 0.94314 0.06245 15.102 < 2e-16 ***
## category_code_LT01_6_count 0.52261 0.15287 3.419 0.000682 ***
## category_code_LT01_7_count 0.62416 0.15161 4.117 4.5e-05 ***
## category_code_LT01_14_count 0.33583 0.32948 1.019 0.308575
## category_code_LT01_16_count 0.69369 1.18476 0.586 0.558470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617188666941992
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0230 -0.7854 -0.0222 0.9762 3.8224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97856 0.08797 113.430 < 2e-16 ***
## category_code_LT01_2_count 0.77788 0.07793 9.982 < 2e-16 ***
## category_code_LT01_5_count 0.95160 0.06197 15.356 < 2e-16 ***
## category_code_LT01_6_count 0.50546 0.15272 3.310 0.001 **
## category_code_LT01_7_count 0.64057 0.15100 4.242 2.65e-05 ***
## category_code_LT01_15_count 0.41785 0.75361 0.554 0.580
## category_code_LT01_16_count 0.63687 1.18378 0.538 0.591
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.606537955460793
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.8202 0.0183 1.0064 3.8413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95961 0.09245 107.734 < 2e-16 ***
## category_code_LT01_2_count 0.86855 0.07425 11.698 < 2e-16 ***
## category_code_LT01_5_count 0.97134 0.06343 15.313 < 2e-16 ***
## category_code_LT01_6_count 0.49594 0.15604 3.178 0.00157 **
## category_code_LT01_8_count -0.13651 0.28000 -0.488 0.62610
## category_code_LT01_9_count 0.44126 0.23153 1.906 0.05725 .
## category_code_LT01_10_count 0.08111 0.11660 0.696 0.48701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6113, Adjusted R-squared: 0.6065
## F-statistic: 128.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.617573514142706
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0316 -0.8120 0.0273 0.9121 3.9107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98601 0.08803 113.436 < 2e-16 ***
## category_code_LT01_2_count 0.67130 0.08998 7.460 3.95e-13 ***
## category_code_LT01_5_count 0.95317 0.06271 15.200 < 2e-16 ***
## category_code_LT01_6_count 0.41173 0.15426 2.669 0.007858 **
## category_code_LT01_8_count -0.11235 0.27607 -0.407 0.684213
## category_code_LT01_9_count 0.39763 0.22734 1.749 0.080901 .
## category_code_LT01_11_count 0.43664 0.11402 3.830 0.000145 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.606253944566589
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0328 -0.8235 0.0089 0.9771 3.8247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97626 0.08929 111.730 < 2e-16 ***
## category_code_LT01_2_count 0.86798 0.07553 11.492 < 2e-16 ***
## category_code_LT01_5_count 0.96904 0.06370 15.213 < 2e-16 ***
## category_code_LT01_6_count 0.50643 0.15521 3.263 0.00118 **
## category_code_LT01_8_count -0.13719 0.28024 -0.490 0.62468
## category_code_LT01_9_count 0.45943 0.23009 1.997 0.04641 *
## category_code_LT01_12_count 0.07606 0.21149 0.360 0.71927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.611, Adjusted R-squared: 0.6063
## F-statistic: 128.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.607107250689775
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0306 -0.8154 0.0092 0.9895 3.8257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97529 0.08920 111.834 < 2e-16 ***
## category_code_LT01_2_count 0.86055 0.07480 11.505 < 2e-16 ***
## category_code_LT01_5_count 0.96720 0.06348 15.235 < 2e-16 ***
## category_code_LT01_6_count 0.51174 0.15406 3.322 0.000961 ***
## category_code_LT01_8_count -0.11743 0.28016 -0.419 0.675297
## category_code_LT01_9_count 0.47317 0.23017 2.056 0.040336 *
## category_code_LT01_13_count 0.27084 0.24765 1.094 0.274657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 491 degrees of freedom
## Multiple R-squared: 0.6119, Adjusted R-squared: 0.6071
## F-statistic: 129 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.607376788422057
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0313 -0.8073 0.0232 0.9569 3.8207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98029 0.08922 111.864 < 2e-16 ***
## category_code_LT01_2_count 0.85463 0.07539 11.337 < 2e-16 ***
## category_code_LT01_5_count 0.96109 0.06387 15.047 < 2e-16 ***
## category_code_LT01_6_count 0.52718 0.15444 3.413 0.000695 ***
## category_code_LT01_8_count -0.14022 0.27972 -0.501 0.616398
## category_code_LT01_9_count 0.43764 0.23045 1.899 0.058142 .
## category_code_LT01_14_count 0.41198 0.33264 1.239 0.216119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 491 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6074
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.60636605498538
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.8238 0.0014 0.9882 3.8245
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97649 0.08928 111.749 < 2e-16 ***
## category_code_LT01_2_count 0.86830 0.07461 11.639 < 2e-16 ***
## category_code_LT01_5_count 0.97132 0.06345 15.309 < 2e-16 ***
## category_code_LT01_6_count 0.50807 0.15446 3.289 0.00108 **
## category_code_LT01_8_count -0.13540 0.28005 -0.483 0.62896
## category_code_LT01_9_count 0.46218 0.23011 2.009 0.04513 *
## category_code_LT01_15_count 0.39637 0.76392 0.519 0.60409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6111, Adjusted R-squared: 0.6064
## F-statistic: 128.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.606276332103741
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0343 -0.8239 0.0099 0.9791 3.8243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97670 0.08929 111.733 < 2e-16 ***
## category_code_LT01_2_count 0.86892 0.07484 11.611 < 2e-16 ***
## category_code_LT01_5_count 0.97053 0.06347 15.292 < 2e-16 ***
## category_code_LT01_6_count 0.51896 0.15502 3.348 0.000877 ***
## category_code_LT01_8_count -0.13965 0.28047 -0.498 0.618759
## category_code_LT01_9_count 0.45681 0.23020 1.984 0.047765 *
## category_code_LT01_16_count 0.47672 1.20210 0.397 0.691853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.611, Adjusted R-squared: 0.6063
## F-statistic: 128.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.61571614813678
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0219 -0.8090 0.0246 0.9273 3.9036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97079 0.09141 109.076 < 2e-16 ***
## category_code_LT01_2_count 0.68723 0.08962 7.668 9.44e-14 ***
## category_code_LT01_5_count 0.96118 0.06269 15.331 < 2e-16 ***
## category_code_LT01_6_count 0.40394 0.15632 2.584 0.0101 *
## category_code_LT01_8_count -0.09942 0.27662 -0.359 0.7194
## category_code_LT01_10_count 0.09384 0.11452 0.819 0.4130
## category_code_LT01_11_count 0.44822 0.11405 3.930 9.71e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6204, Adjusted R-squared: 0.6157
## F-statistic: 133.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.603722289752185
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.8088 -0.0201 0.9769 3.8420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95900 0.09278 107.344 < 2e-16 ***
## category_code_LT01_2_count 0.89318 0.07444 11.999 < 2e-16 ***
## category_code_LT01_5_count 0.97898 0.06371 15.367 < 2e-16 ***
## category_code_LT01_6_count 0.50147 0.15740 3.186 0.00153 **
## category_code_LT01_8_count -0.12255 0.28102 -0.436 0.66297
## category_code_LT01_10_count 0.10560 0.11628 0.908 0.36426
## category_code_LT01_12_count 0.07282 0.21223 0.343 0.73167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6037
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.604354435009704
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0215 -0.8080 -0.0161 0.9849 3.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95889 0.09270 107.429 < 2e-16 ***
## category_code_LT01_2_count 0.88828 0.07356 12.076 < 2e-16 ***
## category_code_LT01_5_count 0.97781 0.06349 15.400 < 2e-16 ***
## category_code_LT01_6_count 0.50773 0.15636 3.247 0.00124 **
## category_code_LT01_8_count -0.10440 0.28106 -0.371 0.71047
## category_code_LT01_10_count 0.10268 0.11623 0.883 0.37745
## category_code_LT01_13_count 0.23589 0.24832 0.950 0.34261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6091, Adjusted R-squared: 0.6044
## F-statistic: 127.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.604794817559573
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.8175 0.0075 0.9514 3.8314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96958 0.09307 107.114 < 2e-16 ***
## category_code_LT01_2_count 0.88055 0.07424 11.861 < 2e-16 ***
## category_code_LT01_5_count 0.97051 0.06396 15.174 < 2e-16 ***
## category_code_LT01_6_count 0.52813 0.15722 3.359 0.000843 ***
## category_code_LT01_8_count -0.12519 0.28052 -0.446 0.655585
## category_code_LT01_10_count 0.07310 0.11937 0.612 0.540542
## category_code_LT01_14_count 0.41205 0.34213 1.204 0.229022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6096, Adjusted R-squared: 0.6048
## F-statistic: 127.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.603761769776703
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0247 -0.8060 -0.0171 0.9819 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95976 0.09280 107.330 < 2e-16 ***
## category_code_LT01_2_count 0.89472 0.07340 12.190 < 2e-16 ***
## category_code_LT01_5_count 0.98117 0.06346 15.461 < 2e-16 ***
## category_code_LT01_6_count 0.50445 0.15663 3.221 0.00136 **
## category_code_LT01_8_count -0.12034 0.28085 -0.428 0.66849
## category_code_LT01_10_count 0.10300 0.11657 0.884 0.37737
## category_code_LT01_15_count 0.31370 0.76844 0.408 0.68329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6038
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.603764064012383
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0241 -0.8106 -0.0207 0.9798 3.8412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95973 0.09279 107.332 < 2e-16 ***
## category_code_LT01_2_count 0.89361 0.07377 12.113 < 2e-16 ***
## category_code_LT01_5_count 0.98031 0.06347 15.444 < 2e-16 ***
## category_code_LT01_6_count 0.51422 0.15738 3.267 0.00116 **
## category_code_LT01_8_count -0.12546 0.28125 -0.446 0.65573
## category_code_LT01_10_count 0.10410 0.11640 0.894 0.37157
## category_code_LT01_16_count 0.49688 1.20692 0.412 0.68074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6038
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.615458979091627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0439 -0.7832 0.0216 0.9354 3.8314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99094 0.08824 113.222 < 2e-16 ***
## category_code_LT01_2_count 0.69484 0.08938 7.774 4.51e-14 ***
## category_code_LT01_5_count 0.96379 0.06287 15.329 < 2e-16 ***
## category_code_LT01_6_count 0.43007 0.15494 2.776 0.00572 **
## category_code_LT01_8_count -0.08857 0.27689 -0.320 0.74920
## category_code_LT01_11_count 0.46719 0.11740 3.979 7.95e-05 ***
## category_code_LT01_12_count -0.12593 0.21517 -0.585 0.55863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 491 degrees of freedom
## Multiple R-squared: 0.6201, Adjusted R-squared: 0.6155
## F-statistic: 133.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.615566630587846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0395 -0.8127 0.0337 0.9349 3.8530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98974 0.08823 113.221 < 2e-16 ***
## category_code_LT01_2_count 0.68865 0.08959 7.687 8.27e-14 ***
## category_code_LT01_5_count 0.95922 0.06277 15.282 < 2e-16 ***
## category_code_LT01_6_count 0.42469 0.15452 2.748 0.006210 **
## category_code_LT01_8_count -0.08484 0.27702 -0.306 0.759528
## category_code_LT01_11_count 0.44475 0.11436 3.889 0.000115 ***
## category_code_LT01_13_count 0.17001 0.24535 0.693 0.488688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6202, Adjusted R-squared: 0.6156
## F-statistic: 133.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.616188871717115
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0385 -0.8035 0.0310 0.9367 3.8506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99360 0.08820 113.308 < 2e-16 ***
## category_code_LT01_2_count 0.67910 0.09014 7.534 2.39e-13 ***
## category_code_LT01_5_count 0.95218 0.06316 15.076 < 2e-16 ***
## category_code_LT01_6_count 0.43796 0.15492 2.827 0.004889 **
## category_code_LT01_8_count -0.10226 0.27647 -0.370 0.711640
## category_code_LT01_11_count 0.44179 0.11421 3.868 0.000124 ***
## category_code_LT01_14_count 0.37145 0.32872 1.130 0.259022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6162
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.615233251935178
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0418 -0.8157 0.0232 0.9324 3.8494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99048 0.08826 113.188 < 2e-16 ***
## category_code_LT01_2_count 0.69185 0.08959 7.722 6.46e-14 ***
## category_code_LT01_5_count 0.96141 0.06274 15.323 < 2e-16 ***
## category_code_LT01_6_count 0.42190 0.15474 2.727 0.00663 **
## category_code_LT01_8_count -0.09593 0.27676 -0.347 0.72904
## category_code_LT01_11_count 0.44918 0.11430 3.930 9.72e-05 ***
## category_code_LT01_15_count 0.17630 0.75662 0.233 0.81585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 491 degrees of freedom
## Multiple R-squared: 0.6199, Adjusted R-squared: 0.6152
## F-statistic: 133.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.615361386821055
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0415 -0.8151 0.0317 0.9328 3.8456
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99081 0.08825 113.206 < 2e-16 ***
## category_code_LT01_2_count 0.68767 0.09017 7.626 1.26e-13 ***
## category_code_LT01_5_count 0.96052 0.06274 15.310 < 2e-16 ***
## category_code_LT01_6_count 0.43067 0.15531 2.773 0.00577 **
## category_code_LT01_8_count -0.10223 0.27711 -0.369 0.71235
## category_code_LT01_11_count 0.45089 0.11405 3.953 8.84e-05 ***
## category_code_LT01_16_count 0.55432 1.18755 0.467 0.64087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 491 degrees of freedom
## Multiple R-squared: 0.62, Adjusted R-squared: 0.6154
## F-statistic: 133.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.603814811124334
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0416 -0.8254 -0.0196 0.9671 3.8207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98023 0.08954 111.465 < 2e-16 ***
## category_code_LT01_2_count 0.89089 0.07466 11.932 < 2e-16 ***
## category_code_LT01_5_count 0.97601 0.06377 15.304 < 2e-16 ***
## category_code_LT01_6_count 0.52400 0.15544 3.371 0.000808 ***
## category_code_LT01_8_count -0.10280 0.28139 -0.365 0.715028
## category_code_LT01_12_count 0.07058 0.21227 0.333 0.739649
## category_code_LT01_13_count 0.24084 0.24847 0.969 0.332872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6086, Adjusted R-squared: 0.6038
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.604540838372006
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.8347 0.0004 0.9458 3.8158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98516 0.08950 111.563 < 2e-16 ***
## category_code_LT01_2_count 0.88037 0.07541 11.674 < 2e-16 ***
## category_code_LT01_5_count 0.96818 0.06414 15.095 < 2e-16 ***
## category_code_LT01_6_count 0.54096 0.15581 3.472 0.000562 ***
## category_code_LT01_8_count -0.12510 0.28074 -0.446 0.656072
## category_code_LT01_12_count 0.05190 0.21279 0.244 0.807420
## category_code_LT01_14_count 0.45360 0.33415 1.357 0.175258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6093, Adjusted R-squared: 0.6045
## F-statistic: 127.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.60324573501717
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0448 -0.8223 -0.0228 0.9681 3.8198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98120 0.08960 111.400 < 2e-16 ***
## category_code_LT01_2_count 0.89616 0.07467 12.002 < 2e-16 ***
## category_code_LT01_5_count 0.97924 0.06375 15.361 < 2e-16 ***
## category_code_LT01_6_count 0.51939 0.15582 3.333 0.000923 ***
## category_code_LT01_8_count -0.11969 0.28117 -0.426 0.670512
## category_code_LT01_12_count 0.07975 0.21235 0.376 0.707406
## category_code_LT01_15_count 0.37096 0.76696 0.484 0.628833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.608, Adjusted R-squared: 0.6032
## F-statistic: 126.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.603228991764834
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0444 -0.8285 -0.0254 0.9698 3.8195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98141 0.08960 111.397 < 2e-16 ***
## category_code_LT01_2_count 0.89535 0.07501 11.937 < 2e-16 ***
## category_code_LT01_5_count 0.97829 0.06376 15.342 < 2e-16 ***
## category_code_LT01_6_count 0.53093 0.15631 3.397 0.000738 ***
## category_code_LT01_8_count -0.12524 0.28158 -0.445 0.656676
## category_code_LT01_12_count 0.07849 0.21231 0.370 0.711792
## category_code_LT01_16_count 0.55696 1.20620 0.462 0.644466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.608, Adjusted R-squared: 0.6032
## F-statistic: 126.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.605244592652785
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0391 -0.8185 -0.0059 0.9465 3.8165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98443 0.08943 111.651 < 2e-16 ***
## category_code_LT01_2_count 0.87336 0.07479 11.678 < 2e-16 ***
## category_code_LT01_5_count 0.96631 0.06397 15.105 < 2e-16 ***
## category_code_LT01_6_count 0.54497 0.15456 3.526 0.000462 ***
## category_code_LT01_8_count -0.10788 0.28076 -0.384 0.700951
## category_code_LT01_13_count 0.23970 0.24790 0.967 0.334063
## category_code_LT01_14_count 0.45712 0.33257 1.375 0.169904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 491 degrees of freedom
## Multiple R-squared: 0.61, Adjusted R-squared: 0.6052
## F-statistic: 128 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.603953256485027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0433 -0.8027 -0.0269 0.9709 3.8205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98045 0.08952 111.487 < 2e-16 ***
## category_code_LT01_2_count 0.89041 0.07382 12.062 < 2e-16 ***
## category_code_LT01_5_count 0.97809 0.06353 15.396 < 2e-16 ***
## category_code_LT01_6_count 0.52515 0.15467 3.395 0.000741 ***
## category_code_LT01_8_count -0.10057 0.28116 -0.358 0.720717
## category_code_LT01_13_count 0.25096 0.24867 1.009 0.313384
## category_code_LT01_15_count 0.40762 0.76728 0.531 0.595479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6087, Adjusted R-squared: 0.604
## F-statistic: 127.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.60392553201773
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0428 -0.8200 -0.0317 0.9721 3.8203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98069 0.08953 111.482 < 2e-16 ***
## category_code_LT01_2_count 0.88965 0.07417 11.994 < 2e-16 ***
## category_code_LT01_5_count 0.97705 0.06355 15.375 < 2e-16 ***
## category_code_LT01_6_count 0.53757 0.15521 3.464 0.00058 ***
## category_code_LT01_8_count -0.10673 0.28155 -0.379 0.70478
## category_code_LT01_13_count 0.24854 0.24849 1.000 0.31771
## category_code_LT01_16_count 0.60042 1.20604 0.498 0.61882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6087, Adjusted R-squared: 0.6039
## F-statistic: 127.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.604651061154676
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0425 -0.8260 0.0070 0.9434 3.8156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98540 0.08949 111.583 < 2e-16 ***
## category_code_LT01_2_count 0.87966 0.07465 11.784 < 2e-16 ***
## category_code_LT01_5_count 0.96974 0.06395 15.164 < 2e-16 ***
## category_code_LT01_6_count 0.54148 0.15494 3.495 0.000517 ***
## category_code_LT01_8_count -0.12416 0.28056 -0.443 0.658301
## category_code_LT01_14_count 0.45727 0.33289 1.374 0.170181
## category_code_LT01_15_count 0.33930 0.76564 0.443 0.657848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6094, Adjusted R-squared: 0.6047
## F-statistic: 127.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.604739898430802
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0419 -0.8260 0.0015 0.9394 3.8151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98584 0.08948 111.594 < 2e-16 ***
## category_code_LT01_2_count 0.87642 0.07525 11.647 < 2e-16 ***
## category_code_LT01_5_count 0.96825 0.06397 15.136 < 2e-16 ***
## category_code_LT01_6_count 0.55452 0.15552 3.566 0.000398 ***
## category_code_LT01_8_count -0.13161 0.28096 -0.468 0.639678
## category_code_LT01_14_count 0.47333 0.33352 1.419 0.156483
## category_code_LT01_16_count 0.66832 1.20661 0.554 0.579911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6095, Adjusted R-squared: 0.6047
## F-statistic: 127.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.603313364987441
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0463 -0.8179 -0.0174 0.9714 3.8193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98168 0.08959 111.411 < 2e-16 ***
## category_code_LT01_2_count 0.89615 0.07404 12.104 < 2e-16 ***
## category_code_LT01_5_count 0.98067 0.06351 15.440 < 2e-16 ***
## category_code_LT01_6_count 0.53329 0.15555 3.429 0.000658 ***
## category_code_LT01_8_count -0.12334 0.28140 -0.438 0.661348
## category_code_LT01_15_count 0.37669 0.76713 0.491 0.623622
## category_code_LT01_16_count 0.57210 1.20668 0.474 0.635633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6081, Adjusted R-squared: 0.6033
## F-statistic: 127 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.617745638811486
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0133 -0.7939 0.0308 0.9369 3.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97016 0.09114 109.390 < 2e-16 ***
## category_code_LT01_2_count 0.66752 0.09016 7.403 5.81e-13 ***
## category_code_LT01_5_count 0.94961 0.06200 15.316 < 2e-16 ***
## category_code_LT01_6_count 0.39430 0.15581 2.531 0.011698 *
## category_code_LT01_9_count 0.37820 0.22862 1.654 0.098705 .
## category_code_LT01_10_count 0.07148 0.11494 0.622 0.534283
## category_code_LT01_11_count 0.43616 0.11399 3.826 0.000147 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6177
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.606432718228389
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0122 -0.8237 0.0091 0.9733 3.8424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95857 0.09243 107.741 <2e-16 ***
## category_code_LT01_2_count 0.86410 0.07580 11.399 <2e-16 ***
## category_code_LT01_5_count 0.96485 0.06302 15.309 <2e-16 ***
## category_code_LT01_6_count 0.48726 0.15686 3.106 0.0020 **
## category_code_LT01_9_count 0.43747 0.23144 1.890 0.0593 .
## category_code_LT01_10_count 0.07935 0.11664 0.680 0.4966
## category_code_LT01_12_count 0.06893 0.21138 0.326 0.7445
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6112, Adjusted R-squared: 0.6064
## F-statistic: 128.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.607298497045033
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0114 -0.8110 0.0042 0.9760 3.8423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95865 0.09233 107.861 < 2e-16 ***
## category_code_LT01_2_count 0.85639 0.07508 11.407 < 2e-16 ***
## category_code_LT01_5_count 0.96347 0.06275 15.354 < 2e-16 ***
## category_code_LT01_6_count 0.49334 0.15576 3.167 0.00163 **
## category_code_LT01_9_count 0.45265 0.23160 1.954 0.05121 .
## category_code_LT01_10_count 0.07509 0.11658 0.644 0.51980
## category_code_LT01_13_count 0.26983 0.24745 1.090 0.27605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 491 degrees of freedom
## Multiple R-squared: 0.612, Adjusted R-squared: 0.6073
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.60731833401469
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0167 -0.8160 0.0162 0.9611 3.8328
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96820 0.09275 107.479 < 2e-16 ***
## category_code_LT01_2_count 0.85366 0.07548 11.310 < 2e-16 ***
## category_code_LT01_5_count 0.95741 0.06325 15.137 < 2e-16 ***
## category_code_LT01_6_count 0.51209 0.15673 3.267 0.00116 **
## category_code_LT01_9_count 0.42409 0.23150 1.832 0.06757 .
## category_code_LT01_10_count 0.05047 0.11958 0.422 0.67315
## category_code_LT01_14_count 0.37621 0.34146 1.102 0.27110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 491 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6073
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.606517732151254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.8243 0.0015 0.9683 3.8415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95947 0.09244 107.734 < 2e-16 ***
## category_code_LT01_2_count 0.86467 0.07486 11.551 < 2e-16 ***
## category_code_LT01_5_count 0.96697 0.06274 15.412 < 2e-16 ***
## category_code_LT01_6_count 0.48954 0.15608 3.137 0.00181 **
## category_code_LT01_9_count 0.44072 0.23151 1.904 0.05754 .
## category_code_LT01_10_count 0.07607 0.11695 0.650 0.51569
## category_code_LT01_15_count 0.35308 0.76605 0.461 0.64507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6113, Adjusted R-squared: 0.6065
## F-statistic: 128.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.606438607640006
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0137 -0.8233 0.0111 0.9702 3.8418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95914 0.09245 107.725 < 2e-16 ***
## category_code_LT01_2_count 0.86529 0.07510 11.522 < 2e-16 ***
## category_code_LT01_5_count 0.96617 0.06277 15.392 < 2e-16 ***
## category_code_LT01_6_count 0.49843 0.15681 3.179 0.00157 **
## category_code_LT01_9_count 0.43543 0.23153 1.881 0.06060 .
## category_code_LT01_10_count 0.07836 0.11674 0.671 0.50242
## category_code_LT01_16_count 0.40512 1.20154 0.337 0.73614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6112, Adjusted R-squared: 0.6064
## F-statistic: 128.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.617705260828306
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0304 -0.8137 0.0224 0.9289 3.9027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98554 0.08799 113.491 < 2e-16 ***
## category_code_LT01_2_count 0.67288 0.09001 7.475 3.56e-13 ***
## category_code_LT01_5_count 0.95220 0.06219 15.310 < 2e-16 ***
## category_code_LT01_6_count 0.41549 0.15450 2.689 0.007404 **
## category_code_LT01_9_count 0.39268 0.22717 1.729 0.084516 .
## category_code_LT01_11_count 0.45367 0.11732 3.867 0.000125 ***
## category_code_LT01_12_count -0.12406 0.21438 -0.579 0.563063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.617967944248668
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0256 -0.8045 0.0469 0.9344 3.9276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98412 0.08796 113.513 < 2e-16 ***
## category_code_LT01_2_count 0.66516 0.09024 7.371 7.26e-13 ***
## category_code_LT01_5_count 0.94724 0.06204 15.269 < 2e-16 ***
## category_code_LT01_6_count 0.41016 0.15402 2.663 0.00800 **
## category_code_LT01_9_count 0.40573 0.22750 1.783 0.07513 .
## category_code_LT01_11_count 0.42987 0.11432 3.760 0.00019 ***
## category_code_LT01_13_count 0.20069 0.24469 0.820 0.41250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.61822209107625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.8053 0.0248 0.9353 3.9190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98790 0.08797 113.539 < 2e-16 ***
## category_code_LT01_2_count 0.65961 0.09066 7.276 1.37e-12 ***
## category_code_LT01_5_count 0.94156 0.06245 15.076 < 2e-16 ***
## category_code_LT01_6_count 0.42193 0.15452 2.731 0.006549 **
## category_code_LT01_9_count 0.37762 0.22761 1.659 0.097745 .
## category_code_LT01_11_count 0.43019 0.11414 3.769 0.000184 ***
## category_code_LT01_14_count 0.32865 0.32864 1.000 0.317797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6228, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.617502446589499
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0281 -0.8194 0.0326 0.9216 3.9223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98499 0.08800 113.462 < 2e-16 ***
## category_code_LT01_2_count 0.66947 0.09023 7.420 5.21e-13 ***
## category_code_LT01_5_count 0.94957 0.06202 15.310 < 2e-16 ***
## category_code_LT01_6_count 0.40685 0.15427 2.637 0.008625 **
## category_code_LT01_9_count 0.39587 0.22728 1.742 0.082176 .
## category_code_LT01_11_count 0.43556 0.11424 3.813 0.000155 ***
## category_code_LT01_15_count 0.20576 0.75456 0.273 0.785206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.617563100771714
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0277 -0.8130 0.0363 0.9228 3.9169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98525 0.08800 113.470 < 2e-16 ***
## category_code_LT01_2_count 0.66672 0.09073 7.348 8.44e-13 ***
## category_code_LT01_5_count 0.94869 0.06204 15.291 < 2e-16 ***
## category_code_LT01_6_count 0.41469 0.15483 2.678 0.007646 **
## category_code_LT01_9_count 0.39134 0.22732 1.722 0.085788 .
## category_code_LT01_11_count 0.43775 0.11399 3.840 0.000139 ***
## category_code_LT01_16_count 0.46160 1.18303 0.390 0.696569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.607042439037334
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0253 -0.8234 0.0164 0.9799 3.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97406 0.08916 111.861 < 2e-16 ***
## category_code_LT01_2_count 0.85606 0.07630 11.219 < 2e-16 ***
## category_code_LT01_5_count 0.96143 0.06304 15.252 < 2e-16 ***
## category_code_LT01_6_count 0.50348 0.15493 3.250 0.00123 **
## category_code_LT01_9_count 0.46969 0.23008 2.041 0.04174 *
## category_code_LT01_12_count 0.06500 0.21126 0.308 0.75846
## category_code_LT01_13_count 0.27392 0.24745 1.107 0.26886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 491 degrees of freedom
## Multiple R-squared: 0.6118, Adjusted R-squared: 0.607
## F-statistic: 129 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.607219849841157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0258 -0.8260 0.0415 0.9627 3.8222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97879 0.08920 111.873 <2e-16 ***
## category_code_LT01_2_count 0.85195 0.07670 11.107 <2e-16 ***
## category_code_LT01_5_count 0.95527 0.06343 15.060 <2e-16 ***
## category_code_LT01_6_count 0.51930 0.15545 3.341 0.0009 ***
## category_code_LT01_9_count 0.43399 0.23037 1.884 0.0602 .
## category_code_LT01_12_count 0.04971 0.21195 0.235 0.8147
## category_code_LT01_14_count 0.40184 0.33398 1.203 0.2295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 491 degrees of freedom
## Multiple R-squared: 0.612, Adjusted R-squared: 0.6072
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.606279096697157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0287 -0.8337 0.0087 0.9772 3.8259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97510 0.08925 111.769 < 2e-16 ***
## category_code_LT01_2_count 0.86329 0.07623 11.324 < 2e-16 ***
## category_code_LT01_5_count 0.96471 0.06303 15.304 < 2e-16 ***
## category_code_LT01_6_count 0.49854 0.15536 3.209 0.00142 **
## category_code_LT01_9_count 0.45800 0.23000 1.991 0.04700 *
## category_code_LT01_12_count 0.07483 0.21141 0.354 0.72354
## category_code_LT01_15_count 0.39781 0.76413 0.521 0.60288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.611, Adjusted R-squared: 0.6063
## F-statistic: 128.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.606173468045057
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0282 -0.8345 0.0122 0.9727 3.8257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97524 0.08926 111.752 < 2e-16 ***
## category_code_LT01_2_count 0.86435 0.07643 11.309 < 2e-16 ***
## category_code_LT01_5_count 0.96387 0.06307 15.283 < 2e-16 ***
## category_code_LT01_6_count 0.50913 0.15585 3.267 0.00116 **
## category_code_LT01_9_count 0.45270 0.23011 1.967 0.04971 *
## category_code_LT01_12_count 0.07311 0.21139 0.346 0.72959
## category_code_LT01_16_count 0.44802 1.20056 0.373 0.70918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6109, Adjusted R-squared: 0.6062
## F-statistic: 128.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.608143201868195
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0236 -0.8044 0.0335 0.9647 3.8230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97797 0.08909 111.993 < 2e-16 ***
## category_code_LT01_2_count 0.84240 0.07620 11.055 < 2e-16 ***
## category_code_LT01_5_count 0.95330 0.06321 15.081 < 2e-16 ***
## category_code_LT01_6_count 0.52294 0.15414 3.393 0.000748 ***
## category_code_LT01_9_count 0.44812 0.23047 1.944 0.052417 .
## category_code_LT01_13_count 0.27194 0.24700 1.101 0.271457
## category_code_LT01_14_count 0.40346 0.33229 1.214 0.225268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 491 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.6081
## F-statistic: 129.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.607234583244946
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.8208 0.0019 0.9787 3.8267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97427 0.08914 111.891 < 2e-16 ***
## category_code_LT01_2_count 0.85445 0.07556 11.308 < 2e-16 ***
## category_code_LT01_5_count 0.96336 0.06276 15.351 < 2e-16 ***
## category_code_LT01_6_count 0.50367 0.15415 3.267 0.00116 **
## category_code_LT01_9_count 0.47327 0.23008 2.057 0.04022 *
## category_code_LT01_13_count 0.28456 0.24767 1.149 0.25114
## category_code_LT01_15_count 0.44230 0.76428 0.579 0.56304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 491 degrees of freedom
## Multiple R-squared: 0.612, Adjusted R-squared: 0.6072
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.607105687667046
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0264 -0.8216 0.0117 0.9726 3.8265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97445 0.08916 111.872 < 2e-16 ***
## category_code_LT01_2_count 0.85566 0.07577 11.293 < 2e-16 ***
## category_code_LT01_5_count 0.96241 0.06279 15.326 < 2e-16 ***
## category_code_LT01_6_count 0.51532 0.15467 3.332 0.000928 ***
## category_code_LT01_9_count 0.46714 0.23017 2.030 0.042940 *
## category_code_LT01_13_count 0.28056 0.24751 1.134 0.257543
## category_code_LT01_16_count 0.50020 1.20010 0.417 0.677005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.412 on 491 degrees of freedom
## Multiple R-squared: 0.6118, Adjusted R-squared: 0.6071
## F-statistic: 129 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.607361663055302
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0271 -0.8193 0.0116 0.9599 3.8219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97902 0.08918 111.899 < 2e-16 ***
## category_code_LT01_2_count 0.85059 0.07600 11.192 < 2e-16 ***
## category_code_LT01_5_count 0.95679 0.06321 15.136 < 2e-16 ***
## category_code_LT01_6_count 0.51921 0.15457 3.359 0.000843 ***
## category_code_LT01_9_count 0.43633 0.23039 1.894 0.058827 .
## category_code_LT01_14_count 0.40466 0.33270 1.216 0.224455
## category_code_LT01_15_count 0.36787 0.76315 0.482 0.629995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 491 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6074
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.607342910586322
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0265 -0.8190 0.0282 0.9559 3.8216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97937 0.08919 111.894 < 2e-16 ***
## category_code_LT01_2_count 0.84947 0.07644 11.114 < 2e-16 ***
## category_code_LT01_5_count 0.95541 0.06325 15.104 < 2e-16 ***
## category_code_LT01_6_count 0.53111 0.15514 3.423 0.00067 ***
## category_code_LT01_9_count 0.42976 0.23051 1.864 0.06287 .
## category_code_LT01_14_count 0.41938 0.33340 1.258 0.20903
## category_code_LT01_16_count 0.54920 1.20162 0.457 0.64783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 491 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6073
## F-statistic: 129.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.606298676886995
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.8245 0.0011 0.9779 3.8255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97550 0.08925 111.773 < 2e-16 ***
## category_code_LT01_2_count 0.86422 0.07555 11.439 < 2e-16 ***
## category_code_LT01_5_count 0.96611 0.06278 15.388 < 2e-16 ***
## category_code_LT01_6_count 0.51071 0.15507 3.294 0.00106 **
## category_code_LT01_9_count 0.45541 0.23011 1.979 0.04837 *
## category_code_LT01_15_count 0.40140 0.76432 0.525 0.59970
## category_code_LT01_16_count 0.46465 1.20094 0.387 0.69899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.413 on 491 degrees of freedom
## Multiple R-squared: 0.6111, Adjusted R-squared: 0.6063
## F-statistic: 128.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.615910322719944
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0211 -0.7922 0.0168 0.9475 3.8953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97031 0.09136 109.128 < 2e-16 ***
## category_code_LT01_2_count 0.68862 0.08963 7.683 8.53e-14 ***
## category_code_LT01_5_count 0.96073 0.06214 15.461 < 2e-16 ***
## category_code_LT01_6_count 0.40820 0.15652 2.608 0.00938 **
## category_code_LT01_10_count 0.09437 0.11449 0.824 0.41019
## category_code_LT01_11_count 0.46599 0.11732 3.972 8.19e-05 ***
## category_code_LT01_12_count -0.13203 0.21490 -0.614 0.53924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6205, Adjusted R-squared: 0.6159
## F-statistic: 133.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.615982036251419
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0176 -0.8115 0.0468 0.9437 3.9147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96993 0.09135 109.136 < 2e-16 ***
## category_code_LT01_2_count 0.68267 0.08982 7.600 1.51e-13 ***
## category_code_LT01_5_count 0.95619 0.06199 15.425 < 2e-16 ***
## category_code_LT01_6_count 0.40338 0.15614 2.583 0.010069 *
## category_code_LT01_10_count 0.09054 0.11452 0.791 0.429564
## category_code_LT01_11_count 0.44293 0.11433 3.874 0.000122 ***
## category_code_LT01_13_count 0.16782 0.24499 0.685 0.493663
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6206, Adjusted R-squared: 0.616
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.616334749062532
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0214 -0.8131 0.0414 0.9288 3.8974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97819 0.09171 108.803 < 2e-16 ***
## category_code_LT01_2_count 0.67637 0.09024 7.495 3.12e-13 ***
## category_code_LT01_5_count 0.94974 0.06248 15.200 < 2e-16 ***
## category_code_LT01_6_count 0.41936 0.15715 2.668 0.007873 **
## category_code_LT01_10_count 0.06690 0.11761 0.569 0.569747
## category_code_LT01_11_count 0.44182 0.11418 3.870 0.000124 ***
## category_code_LT01_14_count 0.32416 0.33776 0.960 0.337671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6163
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.615637545472649
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0192 -0.8154 0.0323 0.9383 3.9111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97033 0.09141 109.068 < 2e-16 ***
## category_code_LT01_2_count 0.68611 0.08981 7.639 1.15e-13 ***
## category_code_LT01_5_count 0.95791 0.06198 15.455 < 2e-16 ***
## category_code_LT01_6_count 0.40053 0.15627 2.563 0.010673 *
## category_code_LT01_10_count 0.09162 0.11482 0.798 0.425287
## category_code_LT01_11_count 0.44778 0.11425 3.919 0.000101 ***
## category_code_LT01_15_count 0.12855 0.75822 0.170 0.865436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6203, Adjusted R-squared: 0.6156
## F-statistic: 133.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.615743655692378
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0190 -0.8139 0.0398 0.9393 3.9082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97077 0.09140 109.088 < 2e-16 ***
## category_code_LT01_2_count 0.68234 0.09038 7.550 2.14e-13 ***
## category_code_LT01_5_count 0.95698 0.06200 15.436 < 2e-16 ***
## category_code_LT01_6_count 0.40798 0.15699 2.599 0.00964 **
## category_code_LT01_10_count 0.09062 0.11466 0.790 0.42969
## category_code_LT01_11_count 0.44910 0.11402 3.939 9.38e-05 ***
## category_code_LT01_16_count 0.48112 1.18680 0.405 0.68537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6204, Adjusted R-squared: 0.6157
## F-statistic: 133.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.604315169253653
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0170 -0.8081 -0.0157 0.9750 3.8428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95811 0.09268 107.448 < 2e-16 ***
## category_code_LT01_2_count 0.88379 0.07508 11.772 < 2e-16 ***
## category_code_LT01_5_count 0.97246 0.06302 15.431 < 2e-16 ***
## category_code_LT01_6_count 0.50016 0.15717 3.182 0.00155 **
## category_code_LT01_10_count 0.10097 0.11624 0.869 0.38547
## category_code_LT01_12_count 0.06334 0.21205 0.299 0.76528
## category_code_LT01_13_count 0.23873 0.24806 0.962 0.33634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6091, Adjusted R-squared: 0.6043
## F-statistic: 127.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.604675879531511
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0222 -0.8349 -0.0030 0.9555 3.8326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96838 0.09306 107.120 < 2e-16 ***
## category_code_LT01_2_count 0.87773 0.07557 11.614 < 2e-16 ***
## category_code_LT01_5_count 0.96514 0.06349 15.202 < 2e-16 ***
## category_code_LT01_6_count 0.52081 0.15820 3.292 0.00107 **
## category_code_LT01_10_count 0.07226 0.11938 0.605 0.54527
## category_code_LT01_12_count 0.04820 0.21264 0.227 0.82076
## category_code_LT01_14_count 0.40266 0.34339 1.173 0.24152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6094, Adjusted R-squared: 0.6047
## F-statistic: 127.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.603705722247442
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0196 -0.8073 -0.0228 0.9711 3.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95887 0.09277 107.345 < 2e-16 ***
## category_code_LT01_2_count 0.88975 0.07504 11.858 < 2e-16 ***
## category_code_LT01_5_count 0.97507 0.06302 15.473 < 2e-16 ***
## category_code_LT01_6_count 0.49580 0.15746 3.149 0.00174 **
## category_code_LT01_10_count 0.10107 0.11660 0.867 0.38647
## category_code_LT01_12_count 0.07168 0.21217 0.338 0.73564
## category_code_LT01_15_count 0.31658 0.76867 0.412 0.68062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6037
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.603692401385887
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0188 -0.8267 -0.0211 0.9699 3.8422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95875 0.09277 107.347 < 2e-16 ***
## category_code_LT01_2_count 0.88902 0.07537 11.795 < 2e-16 ***
## category_code_LT01_5_count 0.97411 0.06305 15.450 < 2e-16 ***
## category_code_LT01_6_count 0.50523 0.15813 3.195 0.00149 **
## category_code_LT01_10_count 0.10231 0.11643 0.879 0.37997
## category_code_LT01_12_count 0.07041 0.21212 0.332 0.74009
## category_code_LT01_16_count 0.47164 1.20532 0.391 0.69574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6037
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.605391382122588
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0215 -0.8292 -0.0055 0.9569 3.8324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96860 0.09297 107.221 < 2e-16 ***
## category_code_LT01_2_count 0.87056 0.07494 11.617 < 2e-16 ***
## category_code_LT01_5_count 0.96370 0.06327 15.230 < 2e-16 ***
## category_code_LT01_6_count 0.52582 0.15696 3.350 0.00087 ***
## category_code_LT01_10_count 0.06859 0.11933 0.575 0.56571
## category_code_LT01_13_count 0.24029 0.24761 0.970 0.33230
## category_code_LT01_14_count 0.40855 0.34182 1.195 0.23257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 491 degrees of freedom
## Multiple R-squared: 0.6102, Adjusted R-squared: 0.6054
## F-statistic: 128.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.604415505827977
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0193 -0.8013 -0.0237 0.9851 3.8419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95903 0.09269 107.443 < 2e-16 ***
## category_code_LT01_2_count 0.88375 0.07420 11.911 < 2e-16 ***
## category_code_LT01_5_count 0.97439 0.06274 15.530 < 2e-16 ***
## category_code_LT01_6_count 0.50208 0.15636 3.211 0.00141 **
## category_code_LT01_10_count 0.09764 0.11654 0.838 0.40257
## category_code_LT01_13_count 0.24776 0.24835 0.998 0.31894
## category_code_LT01_15_count 0.35561 0.76910 0.462 0.64401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6092, Adjusted R-squared: 0.6044
## F-statistic: 127.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.604393789046628
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0184 -0.8068 -0.0267 0.9828 3.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95888 0.09269 107.444 < 2e-16 ***
## category_code_LT01_2_count 0.88300 0.07456 11.843 < 2e-16 ***
## category_code_LT01_5_count 0.97330 0.06278 15.504 < 2e-16 ***
## category_code_LT01_6_count 0.51243 0.15708 3.262 0.00118 **
## category_code_LT01_10_count 0.09909 0.11637 0.852 0.39491
## category_code_LT01_13_count 0.24579 0.24817 0.990 0.32247
## category_code_LT01_16_count 0.52102 1.20540 0.432 0.66576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6092, Adjusted R-squared: 0.6044
## F-statistic: 127.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.604760842939598
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0242 -0.8165 0.0007 0.9529 3.8316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96931 0.09307 107.117 < 2e-16 ***
## category_code_LT01_2_count 0.87734 0.07478 11.732 < 2e-16 ***
## category_code_LT01_5_count 0.96656 0.06327 15.276 < 2e-16 ***
## category_code_LT01_6_count 0.52224 0.15724 3.321 0.000963 ***
## category_code_LT01_10_count 0.06898 0.11967 0.576 0.564592
## category_code_LT01_14_count 0.40840 0.34210 1.194 0.233127
## category_code_LT01_15_count 0.30404 0.76745 0.396 0.692152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6095, Adjusted R-squared: 0.6048
## F-statistic: 127.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.604825489172615
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0236 -0.8317 -0.0003 0.9539 3.8312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96978 0.09308 107.114 < 2e-16 ***
## category_code_LT01_2_count 0.87461 0.07535 11.607 < 2e-16 ***
## category_code_LT01_5_count 0.96501 0.06332 15.240 < 2e-16 ***
## category_code_LT01_6_count 0.53377 0.15808 3.376 0.000792 ***
## category_code_LT01_10_count 0.06830 0.11965 0.571 0.568387
## category_code_LT01_14_count 0.42288 0.34319 1.232 0.218460
## category_code_LT01_16_count 0.58821 1.20750 0.487 0.626382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6096, Adjusted R-squared: 0.6048
## F-statistic: 127.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.603744833624018
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0212 -0.8075 -0.0240 0.9762 3.8414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.95958 0.09279 107.334 < 2e-16 ***
## category_code_LT01_2_count 0.89014 0.07439 11.967 < 2e-16 ***
## category_code_LT01_5_count 0.97629 0.06277 15.554 < 2e-16 ***
## category_code_LT01_6_count 0.50820 0.15733 3.230 0.00132 **
## category_code_LT01_10_count 0.09953 0.11673 0.853 0.39428
## category_code_LT01_15_count 0.32181 0.76893 0.419 0.67575
## category_code_LT01_16_count 0.48631 1.20600 0.403 0.68695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.418 on 491 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.6037
## F-statistic: 127.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.615783019485177
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0392 -0.7790 0.0278 0.9446 3.8440
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98948 0.08817 113.297 < 2e-16 ***
## category_code_LT01_2_count 0.68992 0.08959 7.701 7.52e-14 ***
## category_code_LT01_5_count 0.95919 0.06219 15.423 < 2e-16 ***
## category_code_LT01_6_count 0.42945 0.15473 2.775 0.00572 **
## category_code_LT01_11_count 0.46205 0.11762 3.928 9.78e-05 ***
## category_code_LT01_12_count -0.13080 0.21492 -0.609 0.54308
## category_code_LT01_13_count 0.17604 0.24495 0.719 0.47268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6204, Adjusted R-squared: 0.6158
## F-statistic: 133.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616449096125389
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0379 -0.7924 0.0318 0.9515 3.8407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99339 0.08813 113.388 < 2e-16 ***
## category_code_LT01_2_count 0.68017 0.09013 7.547 2.18e-13 ***
## category_code_LT01_5_count 0.95163 0.06259 15.204 < 2e-16 ***
## category_code_LT01_6_count 0.44364 0.15517 2.859 0.00443 **
## category_code_LT01_11_count 0.46127 0.11736 3.931 9.69e-05 ***
## category_code_LT01_12_count -0.14762 0.21532 -0.686 0.49330
## category_code_LT01_14_count 0.38562 0.32945 1.171 0.24237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.615412172231939
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.7949 0.0254 0.9404 3.8405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99012 0.08821 113.255 < 2e-16 ***
## category_code_LT01_2_count 0.69338 0.08961 7.737 5.82e-14 ***
## category_code_LT01_5_count 0.96094 0.06218 15.453 < 2e-16 ***
## category_code_LT01_6_count 0.42630 0.15497 2.751 0.00617 **
## category_code_LT01_11_count 0.46649 0.11766 3.965 8.44e-05 ***
## category_code_LT01_12_count -0.12704 0.21516 -0.590 0.55517
## category_code_LT01_15_count 0.15614 0.75693 0.206 0.83665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 491 degrees of freedom
## Multiple R-squared: 0.6201, Adjusted R-squared: 0.6154
## F-statistic: 133.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.615532067462692
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0406 -0.7799 0.0262 0.9422 3.8375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99038 0.08820 113.273 < 2e-16 ***
## category_code_LT01_2_count 0.68934 0.09019 7.643 1.12e-13 ***
## category_code_LT01_5_count 0.95993 0.06220 15.432 < 2e-16 ***
## category_code_LT01_6_count 0.43438 0.15547 2.794 0.00541 **
## category_code_LT01_11_count 0.46816 0.11735 3.990 7.63e-05 ***
## category_code_LT01_12_count -0.12793 0.21498 -0.595 0.55206
## category_code_LT01_16_count 0.52444 1.18554 0.442 0.65842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 491 degrees of freedom
## Multiple R-squared: 0.6202, Adjusted R-squared: 0.6155
## F-statistic: 133.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616470972520284
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0333 -0.8042 0.0415 0.9368 3.8641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99197 0.08813 113.379 < 2e-16 ***
## category_code_LT01_2_count 0.67434 0.09035 7.464 3.86e-13 ***
## category_code_LT01_5_count 0.94713 0.06249 15.157 < 2e-16 ***
## category_code_LT01_6_count 0.43653 0.15468 2.822 0.004964 **
## category_code_LT01_11_count 0.43635 0.11449 3.811 0.000156 ***
## category_code_LT01_13_count 0.17270 0.24471 0.706 0.480692
## category_code_LT01_14_count 0.36755 0.32851 1.119 0.263761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.615551508753051
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0368 -0.8257 0.0312 0.9379 3.8639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98893 0.08819 113.263 < 2e-16 ***
## category_code_LT01_2_count 0.68655 0.08983 7.643 1.13e-13 ***
## category_code_LT01_5_count 0.95646 0.06203 15.420 < 2e-16 ***
## category_code_LT01_6_count 0.42055 0.15451 2.722 0.006722 **
## category_code_LT01_11_count 0.44310 0.11462 3.866 0.000126 ***
## category_code_LT01_13_count 0.17814 0.24547 0.726 0.468346
## category_code_LT01_15_count 0.20678 0.75768 0.273 0.785030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6202, Adjusted R-squared: 0.6156
## F-statistic: 133.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.615672163925087
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0362 -0.8135 0.0429 0.9386 3.8596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98919 0.08818 113.281 < 2e-16 ***
## category_code_LT01_2_count 0.68246 0.09042 7.548 2.17e-13 ***
## category_code_LT01_5_count 0.95529 0.06205 15.396 < 2e-16 ***
## category_code_LT01_6_count 0.42960 0.15505 2.771 0.005806 **
## category_code_LT01_11_count 0.44511 0.11434 3.893 0.000113 ***
## category_code_LT01_13_count 0.17905 0.24519 0.730 0.465596
## category_code_LT01_16_count 0.56729 1.18637 0.478 0.632738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 491 degrees of freedom
## Multiple R-squared: 0.6203, Adjusted R-squared: 0.6157
## F-statistic: 133.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616115323179902
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0352 -0.8077 0.0291 0.9402 3.8604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99261 0.08817 113.339 < 2e-16 ***
## category_code_LT01_2_count 0.67776 0.09035 7.502 2.98e-13 ***
## category_code_LT01_5_count 0.94894 0.06248 15.187 < 2e-16 ***
## category_code_LT01_6_count 0.43360 0.15491 2.799 0.005327 **
## category_code_LT01_11_count 0.44115 0.11442 3.855 0.000131 ***
## category_code_LT01_14_count 0.36732 0.32873 1.117 0.264366
## category_code_LT01_15_count 0.15620 0.75584 0.207 0.836367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6161
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616296085246265
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.8055 0.0365 0.9429 3.8580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99302 0.08815 113.366 < 2e-16 ***
## category_code_LT01_2_count 0.67223 0.09105 7.383 6.68e-13 ***
## category_code_LT01_5_count 0.94743 0.06251 15.157 < 2e-16 ***
## category_code_LT01_6_count 0.44334 0.15550 2.851 0.004541 **
## category_code_LT01_11_count 0.44241 0.11418 3.875 0.000121 ***
## category_code_LT01_14_count 0.38014 0.32931 1.154 0.248923
## category_code_LT01_16_count 0.62135 1.18694 0.523 0.600874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6163
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.615301212194342
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0382 -0.8270 0.0322 0.9364 3.8563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98986 0.08822 113.239 < 2e-16 ***
## category_code_LT01_2_count 0.68609 0.09042 7.588 1.65e-13 ***
## category_code_LT01_5_count 0.95724 0.06204 15.430 < 2e-16 ***
## category_code_LT01_6_count 0.42602 0.15524 2.744 0.00629 **
## category_code_LT01_11_count 0.44987 0.11427 3.937 9.45e-05 ***
## category_code_LT01_15_count 0.18427 0.75690 0.243 0.80775
## category_code_LT01_16_count 0.53968 1.18648 0.455 0.64941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.397 on 491 degrees of freedom
## Multiple R-squared: 0.6199, Adjusted R-squared: 0.6153
## F-statistic: 133.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.605157735315112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.8223 0.0014 0.9478 3.8177
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98322 0.08939 111.679 < 2e-16 ***
## category_code_LT01_2_count 0.87068 0.07606 11.448 < 2e-16 ***
## category_code_LT01_5_count 0.96164 0.06347 15.151 < 2e-16 ***
## category_code_LT01_6_count 0.53842 0.15555 3.462 0.000584 ***
## category_code_LT01_12_count 0.04232 0.21260 0.199 0.842319
## category_code_LT01_13_count 0.24342 0.24767 0.983 0.326160
## category_code_LT01_14_count 0.44838 0.33385 1.343 0.179864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 491 degrees of freedom
## Multiple R-squared: 0.6099, Adjusted R-squared: 0.6052
## F-statistic: 128 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.603937853567319
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0384 -0.8271 -0.0296 0.9734 3.8216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97935 0.08948 111.525 < 2e-16 ***
## category_code_LT01_2_count 0.88528 0.07542 11.738 < 2e-16 ***
## category_code_LT01_5_count 0.97268 0.06305 15.427 < 2e-16 ***
## category_code_LT01_6_count 0.51672 0.15556 3.322 0.000961 ***
## category_code_LT01_12_count 0.06999 0.21213 0.330 0.741594
## category_code_LT01_13_count 0.25329 0.24843 1.020 0.308426
## category_code_LT01_15_count 0.41044 0.76742 0.535 0.593013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6087, Adjusted R-squared: 0.6039
## F-statistic: 127.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.603893576912533
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0378 -0.8429 -0.0342 0.9736 3.8214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97951 0.08949 111.519 < 2e-16 ***
## category_code_LT01_2_count 0.88489 0.07574 11.683 < 2e-16 ***
## category_code_LT01_5_count 0.97151 0.06309 15.399 < 2e-16 ***
## category_code_LT01_6_count 0.52888 0.15602 3.390 0.000756 ***
## category_code_LT01_12_count 0.06843 0.21211 0.323 0.747109
## category_code_LT01_13_count 0.25099 0.24828 1.011 0.312553
## category_code_LT01_16_count 0.57904 1.20444 0.481 0.630907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6087, Adjusted R-squared: 0.6039
## F-statistic: 127.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.604539676688752
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0373 -0.8231 -0.0046 0.9484 3.8169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98401 0.08946 111.604 < 2e-16 ***
## category_code_LT01_2_count 0.87660 0.07603 11.530 < 2e-16 ***
## category_code_LT01_5_count 0.96435 0.06348 15.192 < 2e-16 ***
## category_code_LT01_6_count 0.53374 0.15596 3.422 0.000673 ***
## category_code_LT01_12_count 0.05100 0.21273 0.240 0.810620
## category_code_LT01_14_count 0.44694 0.33421 1.337 0.181745
## category_code_LT01_15_count 0.34006 0.76592 0.444 0.657243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6093, Adjusted R-squared: 0.6045
## F-statistic: 127.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.604606024178482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0366 -0.8316 -0.0045 0.9425 3.8166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98436 0.08946 111.612 < 2e-16 ***
## category_code_LT01_2_count 0.87387 0.07657 11.413 < 2e-16 ***
## category_code_LT01_5_count 0.96272 0.06352 15.156 < 2e-16 ***
## category_code_LT01_6_count 0.54636 0.15646 3.492 0.000523 ***
## category_code_LT01_12_count 0.04901 0.21265 0.230 0.817823
## category_code_LT01_14_count 0.46252 0.33480 1.381 0.167757
## category_code_LT01_16_count 0.63706 1.20489 0.529 0.597233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6094, Adjusted R-squared: 0.6046
## F-statistic: 127.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.603265698286625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0406 -0.8285 -0.0382 0.9649 3.8206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98033 0.08956 111.442 < 2e-16 ***
## category_code_LT01_2_count 0.89088 0.07572 11.765 < 2e-16 ***
## category_code_LT01_5_count 0.97434 0.06308 15.445 < 2e-16 ***
## category_code_LT01_6_count 0.52336 0.15638 3.347 0.00088 ***
## category_code_LT01_12_count 0.07743 0.21222 0.365 0.71539
## category_code_LT01_15_count 0.37845 0.76730 0.493 0.62207
## category_code_LT01_16_count 0.54681 1.20495 0.454 0.65017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.419 on 491 degrees of freedom
## Multiple R-squared: 0.6081, Adjusted R-squared: 0.6033
## F-statistic: 127 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.605323147583127
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0358 -0.8102 0.0061 0.9479 3.8175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98343 0.08937 111.707 < 2e-16 ***
## category_code_LT01_2_count 0.86847 0.07545 11.511 < 2e-16 ***
## category_code_LT01_5_count 0.96295 0.06326 15.223 < 2e-16 ***
## category_code_LT01_6_count 0.53764 0.15464 3.477 0.000553 ***
## category_code_LT01_13_count 0.25186 0.24789 1.016 0.310125
## category_code_LT01_14_count 0.45019 0.33256 1.354 0.176441
## category_code_LT01_15_count 0.37955 0.76616 0.495 0.620545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 491 degrees of freedom
## Multiple R-squared: 0.6101, Adjusted R-squared: 0.6053
## F-statistic: 128 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.605387823131821
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0349 -0.8220 0.0039 0.9456 3.8172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98380 0.08937 111.716 < 2e-16 ***
## category_code_LT01_2_count 0.86557 0.07603 11.385 < 2e-16 ***
## category_code_LT01_5_count 0.96114 0.06330 15.183 < 2e-16 ***
## category_code_LT01_6_count 0.55122 0.15517 3.552 0.000419 ***
## category_code_LT01_13_count 0.25090 0.24770 1.013 0.311603
## category_code_LT01_14_count 0.46685 0.33315 1.401 0.161748
## category_code_LT01_16_count 0.68780 1.20476 0.571 0.568329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.415 on 491 degrees of freedom
## Multiple R-squared: 0.6102, Adjusted R-squared: 0.6054
## F-statistic: 128.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.604049603382353
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0395 -0.8184 -0.0367 0.9728 3.8212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97977 0.08947 111.544 < 2e-16 ***
## category_code_LT01_2_count 0.88389 0.07495 11.792 < 2e-16 ***
## category_code_LT01_5_count 0.97358 0.06280 15.502 < 2e-16 ***
## category_code_LT01_6_count 0.53003 0.15521 3.415 0.000691 ***
## category_code_LT01_13_count 0.26130 0.24852 1.051 0.293589
## category_code_LT01_15_count 0.41877 0.76763 0.546 0.585629
## category_code_LT01_16_count 0.59939 1.20491 0.497 0.619092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 491 degrees of freedom
## Multiple R-squared: 0.6088, Adjusted R-squared: 0.604
## F-statistic: 127.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.604729993589637
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0378 -0.8159 -0.0041 0.9410 3.8163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98462 0.08944 111.635 < 2e-16 ***
## category_code_LT01_2_count 0.87267 0.07587 11.502 < 2e-16 ***
## category_code_LT01_5_count 0.96422 0.06330 15.232 < 2e-16 ***
## category_code_LT01_6_count 0.54675 0.15554 3.515 0.00048 ***
## category_code_LT01_14_count 0.46601 0.33350 1.397 0.16295
## category_code_LT01_15_count 0.34856 0.76589 0.455 0.64923
## category_code_LT01_16_count 0.65343 1.20528 0.542 0.58797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.416 on 491 degrees of freedom
## Multiple R-squared: 0.6095, Adjusted R-squared: 0.6047
## F-statistic: 127.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.611368194346012
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0330 -0.7743 0.0155 0.9430 3.8296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97138 0.09188 108.531 < 2e-16 ***
## category_code_LT01_2_count 0.86608 0.07127 12.153 < 2e-16 ***
## category_code_LT01_5_count 0.97638 0.06273 15.565 < 2e-16 ***
## category_code_LT01_7_count 0.62017 0.15347 4.041 6.18e-05 ***
## category_code_LT01_8_count -0.13716 0.27820 -0.493 0.622
## category_code_LT01_9_count 0.37606 0.23109 1.627 0.104
## category_code_LT01_10_count 0.10295 0.11483 0.897 0.370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6114
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.619206895489007
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0502 -0.7849 0.0046 0.9102 3.8019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99903 0.08766 114.066 < 2e-16 ***
## category_code_LT01_2_count 0.70292 0.08713 8.067 5.56e-15 ***
## category_code_LT01_5_count 0.96131 0.06227 15.439 < 2e-16 ***
## category_code_LT01_7_count 0.48107 0.15809 3.043 0.00247 **
## category_code_LT01_8_count -0.11137 0.27540 -0.404 0.68611
## category_code_LT01_9_count 0.36158 0.22756 1.589 0.11273
## category_code_LT01_11_count 0.38675 0.11699 3.306 0.00102 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.611082469242403
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0518 -0.7824 -0.0029 0.9530 3.8082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99280 0.08858 112.814 < 2e-16 ***
## category_code_LT01_2_count 0.86287 0.07298 11.824 < 2e-16 ***
## category_code_LT01_5_count 0.97257 0.06307 15.421 < 2e-16 ***
## category_code_LT01_7_count 0.62892 0.15310 4.108 4.68e-05 ***
## category_code_LT01_8_count -0.13976 0.27846 -0.502 0.6160
## category_code_LT01_9_count 0.39837 0.22969 1.734 0.0835 .
## category_code_LT01_12_count 0.13895 0.20889 0.665 0.5062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.610953989380001
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0538 -0.7917 -0.0005 0.9650 3.8081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99286 0.08859 112.795 < 2e-16 ***
## category_code_LT01_2_count 0.87097 0.07103 12.262 < 2e-16 ***
## category_code_LT01_5_count 0.97529 0.06282 15.525 < 2e-16 ***
## category_code_LT01_7_count 0.61885 0.15472 4.000 7.32e-05 ***
## category_code_LT01_8_count -0.12392 0.27877 -0.445 0.657
## category_code_LT01_9_count 0.40801 0.23028 1.772 0.077 .
## category_code_LT01_13_count 0.13182 0.24904 0.529 0.597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.611
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.611041759867098
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0546 -0.7852 0.0038 0.9500 3.8053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99563 0.08865 112.752 <2e-16 ***
## category_code_LT01_2_count 0.86892 0.07128 12.190 <2e-16 ***
## category_code_LT01_5_count 0.97245 0.06313 15.404 <2e-16 ***
## category_code_LT01_7_count 0.62216 0.15369 4.048 6e-05 ***
## category_code_LT01_8_count -0.13471 0.27829 -0.484 0.6285
## category_code_LT01_9_count 0.39048 0.23016 1.697 0.0904 .
## category_code_LT01_14_count 0.20726 0.33144 0.625 0.5320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.611
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.611185037566631
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0553 -0.7829 -0.0086 0.9600 3.8076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99333 0.08856 112.841 < 2e-16 ***
## category_code_LT01_2_count 0.86599 0.07160 12.095 < 2e-16 ***
## category_code_LT01_5_count 0.97678 0.06274 15.569 < 2e-16 ***
## category_code_LT01_7_count 0.63183 0.15306 4.128 4.3e-05 ***
## category_code_LT01_8_count -0.13565 0.27824 -0.488 0.6261
## category_code_LT01_9_count 0.40238 0.22969 1.752 0.0804 .
## category_code_LT01_15_count 0.57333 0.75799 0.756 0.4498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6159, Adjusted R-squared: 0.6112
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.610754205012402
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0555 -0.7908 -0.0064 0.9598 3.8073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99361 0.08862 112.767 < 2e-16 ***
## category_code_LT01_2_count 0.87382 0.07112 12.286 < 2e-16 ***
## category_code_LT01_5_count 0.97668 0.06278 15.558 < 2e-16 ***
## category_code_LT01_7_count 0.63136 0.15320 4.121 4.42e-05 ***
## category_code_LT01_8_count -0.13486 0.27872 -0.484 0.6287
## category_code_LT01_9_count 0.39846 0.22988 1.733 0.0837 .
## category_code_LT01_16_count 0.19910 1.18947 0.167 0.8671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.61801628036261
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7661 0.0146 0.9327 3.8220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97897 0.09112 109.519 < 2e-16 ***
## category_code_LT01_2_count 0.71276 0.08696 8.197 2.17e-15 ***
## category_code_LT01_5_count 0.96771 0.06220 15.557 < 2e-16 ***
## category_code_LT01_7_count 0.49186 0.15814 3.110 0.001978 **
## category_code_LT01_8_count -0.10275 0.27575 -0.373 0.709575
## category_code_LT01_10_count 0.11238 0.11316 0.993 0.321137
## category_code_LT01_11_count 0.39253 0.11708 3.353 0.000862 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.609591888515459
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0354 -0.7782 -0.0058 0.9361 3.8298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97120 0.09209 108.282 < 2e-16 ***
## category_code_LT01_2_count 0.87742 0.07238 12.123 < 2e-16 ***
## category_code_LT01_5_count 0.98008 0.06301 15.554 < 2e-16 ***
## category_code_LT01_7_count 0.64370 0.15307 4.205 3.1e-05 ***
## category_code_LT01_8_count -0.13014 0.27891 -0.467 0.641
## category_code_LT01_10_count 0.12121 0.11444 1.059 0.290
## category_code_LT01_12_count 0.13283 0.20945 0.634 0.526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.609387819105603
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0374 -0.7729 0.0080 0.9316 3.8299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97105 0.09211 108.252 < 2e-16 ***
## category_code_LT01_2_count 0.88647 0.07036 12.599 < 2e-16 ***
## category_code_LT01_5_count 0.98317 0.06275 15.667 < 2e-16 ***
## category_code_LT01_7_count 0.63716 0.15450 4.124 4.37e-05 ***
## category_code_LT01_8_count -0.11683 0.27929 -0.418 0.676
## category_code_LT01_10_count 0.12312 0.11441 1.076 0.282
## category_code_LT01_13_count 0.09497 0.24901 0.381 0.703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6094
## F-statistic: 130.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.609482971294254
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0401 -0.7851 0.0076 0.9259 3.8253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97569 0.09252 107.819 < 2e-16 ***
## category_code_LT01_2_count 0.88452 0.07058 12.533 < 2e-16 ***
## category_code_LT01_5_count 0.98042 0.06311 15.536 < 2e-16 ***
## category_code_LT01_7_count 0.63889 0.15355 4.161 3.74e-05 ***
## category_code_LT01_8_count -0.12479 0.27875 -0.448 0.655
## category_code_LT01_10_count 0.11142 0.11700 0.952 0.341
## category_code_LT01_14_count 0.17459 0.33908 0.515 0.607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.609595038406819
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0395 -0.7734 -0.0055 0.9266 3.8285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97246 0.09211 108.269 < 2e-16 ***
## category_code_LT01_2_count 0.88200 0.07089 12.443 < 2e-16 ***
## category_code_LT01_5_count 0.98422 0.06269 15.700 < 2e-16 ***
## category_code_LT01_7_count 0.64700 0.15308 4.227 2.83e-05 ***
## category_code_LT01_8_count -0.12554 0.27872 -0.450 0.653
## category_code_LT01_10_count 0.11797 0.11476 1.028 0.304
## category_code_LT01_15_count 0.48572 0.76216 0.637 0.524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.609297008802238
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0385 -0.7875 -0.0016 0.9268 3.8294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97153 0.09215 108.213 < 2e-16 ***
## category_code_LT01_2_count 0.88751 0.07059 12.573 < 2e-16 ***
## category_code_LT01_5_count 0.98399 0.06272 15.689 < 2e-16 ***
## category_code_LT01_7_count 0.64585 0.15316 4.217 2.95e-05 ***
## category_code_LT01_8_count -0.12581 0.27916 -0.451 0.652
## category_code_LT01_10_count 0.12340 0.11447 1.078 0.282
## category_code_LT01_16_count 0.21087 1.19201 0.177 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6093
## F-statistic: 130.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.617278723275712
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0605 -0.7515 0.0125 0.9191 3.7974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00358 0.08785 113.871 < 2e-16 ***
## category_code_LT01_2_count 0.72392 0.08659 8.360 6.5e-16 ***
## category_code_LT01_5_count 0.96955 0.06248 15.519 < 2e-16 ***
## category_code_LT01_7_count 0.50192 0.15812 3.174 0.001597 **
## category_code_LT01_8_count -0.09398 0.27624 -0.340 0.733849
## category_code_LT01_11_count 0.40225 0.12125 3.318 0.000976 ***
## category_code_LT01_12_count -0.04191 0.21448 -0.195 0.845173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.617310750463326
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0589 -0.7670 0.0143 0.9176 3.7979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00309 0.08784 113.878 < 2e-16 ***
## category_code_LT01_2_count 0.72150 0.08658 8.334 7.92e-16 ***
## category_code_LT01_5_count 0.96789 0.06230 15.537 < 2e-16 ***
## category_code_LT01_7_count 0.49837 0.15899 3.135 0.001824 **
## category_code_LT01_8_count -0.09184 0.27642 -0.332 0.739845
## category_code_LT01_11_count 0.39487 0.11722 3.369 0.000815 ***
## category_code_LT01_13_count 0.06942 0.24658 0.282 0.778408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.61756679621365
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0586 -0.7654 0.0068 0.9140 3.7955
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00547 0.08787 113.866 < 2e-16 ***
## category_code_LT01_2_count 0.71686 0.08692 8.247 1.5e-15 ***
## category_code_LT01_5_count 0.96406 0.06263 15.394 < 2e-16 ***
## category_code_LT01_7_count 0.49538 0.15832 3.129 0.001859 **
## category_code_LT01_8_count -0.09918 0.27587 -0.360 0.719365
## category_code_LT01_11_count 0.39388 0.11715 3.362 0.000833 ***
## category_code_LT01_14_count 0.20961 0.32814 0.639 0.523256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.61741951952953
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0596 -0.7508 -0.0078 0.9210 3.7977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00326 0.08782 113.902 < 2e-16 ***
## category_code_LT01_2_count 0.71927 0.08677 8.289 1.1e-15 ***
## category_code_LT01_5_count 0.96875 0.06225 15.563 < 2e-16 ***
## category_code_LT01_7_count 0.50633 0.15790 3.207 0.001431 **
## category_code_LT01_8_count -0.09856 0.27592 -0.357 0.721101
## category_code_LT01_11_count 0.39178 0.11749 3.335 0.000918 ***
## category_code_LT01_15_count 0.35283 0.75416 0.468 0.640098
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.617300638586002
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0597 -0.7509 0.0135 0.9214 3.7973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00367 0.08785 113.875 < 2e-16 ***
## category_code_LT01_2_count 0.72040 0.08698 8.282 1.16e-15 ***
## category_code_LT01_5_count 0.96833 0.06226 15.553 < 2e-16 ***
## category_code_LT01_7_count 0.50457 0.15786 3.196 0.00148 **
## category_code_LT01_8_count -0.10010 0.27629 -0.362 0.71728
## category_code_LT01_11_count 0.39648 0.11714 3.385 0.00077 ***
## category_code_LT01_16_count 0.30357 1.17902 0.257 0.79692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.608818101467303
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0612 -0.7866 -0.0218 0.9387 3.8039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99709 0.08880 112.581 < 2e-16 ***
## category_code_LT01_2_count 0.88682 0.07187 12.339 < 2e-16 ***
## category_code_LT01_5_count 0.97999 0.06311 15.529 < 2e-16 ***
## category_code_LT01_7_count 0.64984 0.15407 4.218 2.94e-05 ***
## category_code_LT01_8_count -0.11739 0.27966 -0.420 0.675
## category_code_LT01_12_count 0.13924 0.20959 0.664 0.507
## category_code_LT01_13_count 0.09601 0.24924 0.385 0.700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.609069183626486
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0614 -0.7923 -0.0157 0.9156 3.8012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99978 0.08884 112.564 < 2e-16 ***
## category_code_LT01_2_count 0.88270 0.07221 12.224 < 2e-16 ***
## category_code_LT01_5_count 0.97627 0.06341 15.397 < 2e-16 ***
## category_code_LT01_7_count 0.64809 0.15326 4.229 2.8e-05 ***
## category_code_LT01_8_count -0.12624 0.27905 -0.452 0.651
## category_code_LT01_12_count 0.13056 0.21007 0.622 0.535
## category_code_LT01_14_count 0.22655 0.33264 0.681 0.496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.6091
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609131844996843
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0621 -0.7869 -0.0254 0.9302 3.8036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99736 0.08876 112.634 < 2e-16 ***
## category_code_LT01_2_count 0.88021 0.07261 12.123 < 2e-16 ***
## category_code_LT01_5_count 0.98086 0.06304 15.559 < 2e-16 ***
## category_code_LT01_7_count 0.65928 0.15257 4.321 1.88e-05 ***
## category_code_LT01_8_count -0.12710 0.27904 -0.455 0.649
## category_code_LT01_12_count 0.14415 0.20943 0.688 0.492
## category_code_LT01_15_count 0.55983 0.75998 0.737 0.462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6091
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.608742323065805
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0623 -0.7872 -0.0282 0.9385 3.8033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99768 0.08881 112.569 < 2e-16 ***
## category_code_LT01_2_count 0.88709 0.07223 12.282 < 2e-16 ***
## category_code_LT01_5_count 0.98067 0.06308 15.547 < 2e-16 ***
## category_code_LT01_7_count 0.65880 0.15268 4.315 1.93e-05 ***
## category_code_LT01_8_count -0.12739 0.27953 -0.456 0.649
## category_code_LT01_12_count 0.14279 0.20956 0.681 0.496
## category_code_LT01_16_count 0.27519 1.19231 0.231 0.818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6087
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.608894041468702
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0636 -0.7897 -0.0090 0.9202 3.8008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00018 0.08885 112.548 < 2e-16 ***
## category_code_LT01_2_count 0.89100 0.07030 12.674 < 2e-16 ***
## category_code_LT01_5_count 0.97889 0.06320 15.488 < 2e-16 ***
## category_code_LT01_7_count 0.64046 0.15475 4.139 4.11e-05 ***
## category_code_LT01_8_count -0.11271 0.27942 -0.403 0.687
## category_code_LT01_13_count 0.10156 0.24910 0.408 0.684
## category_code_LT01_14_count 0.24301 0.33169 0.733 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6089
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.608913437621905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0647 -0.7746 -0.0190 0.9308 3.8033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99762 0.08878 112.606 < 2e-16 ***
## category_code_LT01_2_count 0.88984 0.07052 12.619 < 2e-16 ***
## category_code_LT01_5_count 0.98414 0.06279 15.675 < 2e-16 ***
## category_code_LT01_7_count 0.65174 0.15404 4.231 2.78e-05 ***
## category_code_LT01_8_count -0.11204 0.27940 -0.401 0.689
## category_code_LT01_13_count 0.11135 0.24946 0.446 0.656
## category_code_LT01_15_count 0.57022 0.76122 0.749 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6089
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.608509262631015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0649 -0.7754 -0.0184 0.9370 3.8030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99796 0.08884 112.540 < 2e-16 ***
## category_code_LT01_2_count 0.89699 0.07009 12.798 < 2e-16 ***
## category_code_LT01_5_count 0.98399 0.06282 15.663 < 2e-16 ***
## category_code_LT01_7_count 0.65191 0.15415 4.229 2.8e-05 ***
## category_code_LT01_8_count -0.11290 0.27986 -0.403 0.687
## category_code_LT01_13_count 0.10335 0.24939 0.414 0.679
## category_code_LT01_16_count 0.27637 1.19322 0.232 0.817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6132, Adjusted R-squared: 0.6085
## F-statistic: 129.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609163863466448
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0647 -0.7900 -0.0186 0.9242 3.8005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00044 0.08882 112.594 < 2e-16 ***
## category_code_LT01_2_count 0.88548 0.07091 12.487 < 2e-16 ***
## category_code_LT01_5_count 0.98007 0.06313 15.525 < 2e-16 ***
## category_code_LT01_7_count 0.65061 0.15325 4.245 2.61e-05 ***
## category_code_LT01_8_count -0.12237 0.27884 -0.439 0.661
## category_code_LT01_14_count 0.23777 0.33165 0.717 0.474
## category_code_LT01_15_count 0.54028 0.76003 0.711 0.478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6092
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608815250266855
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0648 -0.7905 -0.0167 0.9196 3.8001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00089 0.08887 112.532 < 2e-16 ***
## category_code_LT01_2_count 0.89132 0.07065 12.617 < 2e-16 ***
## category_code_LT01_5_count 0.97961 0.06317 15.508 < 2e-16 ***
## category_code_LT01_7_count 0.64982 0.15333 4.238 2.69e-05 ***
## category_code_LT01_8_count -0.12336 0.27932 -0.442 0.659
## category_code_LT01_14_count 0.24775 0.33228 0.746 0.456
## category_code_LT01_16_count 0.30977 1.19395 0.259 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.60880258651412
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0660 -0.7754 -0.0308 0.9309 3.8027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99830 0.08880 112.590 < 2e-16 ***
## category_code_LT01_2_count 0.89092 0.07077 12.589 < 2e-16 ***
## category_code_LT01_5_count 0.98508 0.06275 15.699 < 2e-16 ***
## category_code_LT01_7_count 0.66207 0.15266 4.337 1.75e-05 ***
## category_code_LT01_8_count -0.12304 0.27932 -0.440 0.660
## category_code_LT01_15_count 0.55908 0.76079 0.735 0.463
## category_code_LT01_16_count 0.29232 1.19287 0.245 0.807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.619593850452338
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0263 -0.7676 0.0325 0.9184 3.8229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97806 0.09090 109.771 < 2e-16 ***
## category_code_LT01_2_count 0.69539 0.08753 7.944 1.35e-14 ***
## category_code_LT01_5_count 0.95707 0.06151 15.560 < 2e-16 ***
## category_code_LT01_7_count 0.46994 0.15823 2.970 0.00312 **
## category_code_LT01_9_count 0.33745 0.22878 1.475 0.14085
## category_code_LT01_10_count 0.09253 0.11362 0.814 0.41585
## category_code_LT01_11_count 0.38546 0.11694 3.296 0.00105 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6196
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.611471222866999
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0258 -0.7699 0.0243 0.9548 3.8307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97021 0.09183 108.570 < 2e-16 ***
## category_code_LT01_2_count 0.85525 0.07355 11.628 < 2e-16 ***
## category_code_LT01_5_count 0.96763 0.06236 15.517 < 2e-16 ***
## category_code_LT01_7_count 0.61624 0.15338 4.018 6.8e-05 ***
## category_code_LT01_9_count 0.37196 0.23096 1.611 0.108
## category_code_LT01_10_count 0.09906 0.11489 0.862 0.389
## category_code_LT01_12_count 0.12758 0.20880 0.611 0.541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6115
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.611397959468372
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0278 -0.7937 0.0305 0.9508 3.8308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97017 0.09184 108.559 < 2e-16 ***
## category_code_LT01_2_count 0.86222 0.07169 12.027 < 2e-16 ***
## category_code_LT01_5_count 0.97052 0.06205 15.642 < 2e-16 ***
## category_code_LT01_7_count 0.60622 0.15489 3.914 0.000104 ***
## category_code_LT01_9_count 0.38169 0.23161 1.648 0.100005
## category_code_LT01_10_count 0.10005 0.11486 0.871 0.384135
## category_code_LT01_13_count 0.13171 0.24859 0.530 0.596466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6161, Adjusted R-squared: 0.6114
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.611333529927677
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0304 -0.7872 0.0284 0.9378 3.8268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97414 0.09227 108.097 < 2e-16 ***
## category_code_LT01_2_count 0.86271 0.07174 12.025 < 2e-16 ***
## category_code_LT01_5_count 0.96856 0.06241 15.519 < 2e-16 ***
## category_code_LT01_7_count 0.61255 0.15382 3.982 7.86e-05 ***
## category_code_LT01_9_count 0.36839 0.23119 1.593 0.112
## category_code_LT01_10_count 0.09112 0.11733 0.777 0.438
## category_code_LT01_14_count 0.15113 0.33853 0.446 0.655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6113
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.611536146185434
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7681 0.0043 0.9480 3.8294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97159 0.09185 108.567 < 2e-16 ***
## category_code_LT01_2_count 0.85867 0.07216 11.900 < 2e-16 ***
## category_code_LT01_5_count 0.97166 0.06199 15.673 < 2e-16 ***
## category_code_LT01_7_count 0.61936 0.15337 4.038 6.25e-05 ***
## category_code_LT01_9_count 0.37658 0.23101 1.630 0.104
## category_code_LT01_10_count 0.09513 0.11522 0.826 0.409
## category_code_LT01_15_count 0.51323 0.76045 0.675 0.500
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6162, Adjusted R-squared: 0.6115
## F-statistic: 131.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.611185847896239
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0288 -0.7724 0.0248 0.9467 3.8305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97044 0.09189 108.504 < 2e-16 ***
## category_code_LT01_2_count 0.86544 0.07177 12.058 < 2e-16 ***
## category_code_LT01_5_count 0.97159 0.06203 15.663 < 2e-16 ***
## category_code_LT01_7_count 0.61816 0.15348 4.028 6.53e-05 ***
## category_code_LT01_9_count 0.37190 0.23112 1.609 0.108
## category_code_LT01_10_count 0.10145 0.11491 0.883 0.378
## category_code_LT01_16_count 0.13396 1.18803 0.113 0.910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6159, Adjusted R-squared: 0.6112
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.619112956169219
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0476 -0.7877 0.0103 0.9134 3.8028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99821 0.08764 114.084 < 2e-16 ***
## category_code_LT01_2_count 0.70368 0.08730 8.061 5.82e-15 ***
## category_code_LT01_5_count 0.95864 0.06181 15.510 < 2e-16 ***
## category_code_LT01_7_count 0.47667 0.15824 3.012 0.00273 **
## category_code_LT01_9_count 0.35827 0.22746 1.575 0.11589
## category_code_LT01_11_count 0.39425 0.12104 3.257 0.00120 **
## category_code_LT01_12_count -0.04401 0.21374 -0.206 0.83696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.61921643337732
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0455 -0.7871 0.0141 0.9109 3.8034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99754 0.08762 114.102 < 2e-16 ***
## category_code_LT01_2_count 0.70015 0.08732 8.018 7.93e-15 ***
## category_code_LT01_5_count 0.95662 0.06157 15.537 < 2e-16 ***
## category_code_LT01_7_count 0.47023 0.15920 2.954 0.00329 **
## category_code_LT01_9_count 0.36545 0.22804 1.603 0.10967
## category_code_LT01_11_count 0.38572 0.11707 3.295 0.00106 **
## category_code_LT01_13_count 0.10323 0.24619 0.419 0.67517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.61930435651925
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0458 -0.7893 0.0230 0.8967 3.8012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99978 0.08767 114.057 < 2e-16 ***
## category_code_LT01_2_count 0.69792 0.08755 7.972 1.11e-14 ***
## category_code_LT01_5_count 0.95380 0.06191 15.407 < 2e-16 ***
## category_code_LT01_7_count 0.47191 0.15842 2.979 0.00304 **
## category_code_LT01_9_count 0.35083 0.22784 1.540 0.12426
## category_code_LT01_11_count 0.38617 0.11699 3.301 0.00103 **
## category_code_LT01_14_count 0.17640 0.32799 0.538 0.59093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6193
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.619269691949542
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0464 -0.7873 -0.0004 0.9196 3.8032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99779 0.08761 114.118 < 2e-16 ***
## category_code_LT01_2_count 0.69863 0.08749 7.985 1e-14 ***
## category_code_LT01_5_count 0.95759 0.06153 15.562 < 2e-16 ***
## category_code_LT01_7_count 0.48104 0.15803 3.044 0.00246 **
## category_code_LT01_9_count 0.36067 0.22746 1.586 0.11347
## category_code_LT01_11_count 0.38322 0.11733 3.266 0.00117 **
## category_code_LT01_15_count 0.37206 0.75238 0.495 0.62117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6193
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.619108478426411
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0466 -0.7877 0.0149 0.9174 3.8028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99815 0.08764 114.087 < 2e-16 ***
## category_code_LT01_2_count 0.70080 0.08765 7.996 9.3e-15 ***
## category_code_LT01_5_count 0.95727 0.06156 15.551 < 2e-16 ***
## category_code_LT01_7_count 0.47915 0.15801 3.032 0.002555 **
## category_code_LT01_9_count 0.35710 0.22757 1.569 0.117249
## category_code_LT01_11_count 0.38816 0.11699 3.318 0.000974 ***
## category_code_LT01_16_count 0.22490 1.17527 0.191 0.848326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.611111633218032
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0460 -0.7805 0.0109 0.9401 3.8100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99097 0.08853 112.851 < 2e-16 ***
## category_code_LT01_2_count 0.85936 0.07336 11.714 < 2e-16 ***
## category_code_LT01_5_count 0.96683 0.06242 15.489 < 2e-16 ***
## category_code_LT01_7_count 0.61456 0.15457 3.976 8.06e-05 ***
## category_code_LT01_9_count 0.40345 0.23015 1.753 0.0802 .
## category_code_LT01_12_count 0.13152 0.20881 0.630 0.5291
## category_code_LT01_13_count 0.13363 0.24867 0.537 0.5913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.611140629999185
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0467 -0.7990 0.0179 0.9315 3.8075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99347 0.08860 112.791 < 2e-16 ***
## category_code_LT01_2_count 0.85846 0.07349 11.682 < 2e-16 ***
## category_code_LT01_5_count 0.96421 0.06272 15.373 < 2e-16 ***
## category_code_LT01_7_count 0.61863 0.15359 4.028 6.52e-05 ***
## category_code_LT01_9_count 0.38634 0.23001 1.680 0.0937 .
## category_code_LT01_12_count 0.12548 0.20936 0.599 0.5492
## category_code_LT01_14_count 0.18961 0.33239 0.570 0.5686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.611338842024135
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0470 -0.7809 -0.0004 0.9358 3.8097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99130 0.08850 112.892 < 2e-16 ***
## category_code_LT01_2_count 0.85392 0.07402 11.537 < 2e-16 ***
## category_code_LT01_5_count 0.96776 0.06237 15.517 < 2e-16 ***
## category_code_LT01_7_count 0.62741 0.15297 4.102 4.8e-05 ***
## category_code_LT01_9_count 0.39733 0.22952 1.731 0.0841 .
## category_code_LT01_12_count 0.13717 0.20868 0.657 0.5113
## category_code_LT01_15_count 0.57514 0.75784 0.759 0.4483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6113
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610902172455882
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0473 -0.7811 0.0060 0.9311 3.8094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99158 0.08856 112.820 < 2e-16 ***
## category_code_LT01_2_count 0.86203 0.07355 11.720 < 2e-16 ***
## category_code_LT01_5_count 0.96775 0.06241 15.506 < 2e-16 ***
## category_code_LT01_7_count 0.62692 0.15309 4.095 4.94e-05 ***
## category_code_LT01_9_count 0.39353 0.22973 1.713 0.0873 .
## category_code_LT01_12_count 0.13548 0.20882 0.649 0.5168
## category_code_LT01_16_count 0.18513 1.18797 0.156 0.8762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6156, Adjusted R-squared: 0.6109
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611100286353172
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0487 -0.8216 0.0158 0.9308 3.8072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99377 0.08860 112.795 < 2e-16 ***
## category_code_LT01_2_count 0.86466 0.07174 12.052 < 2e-16 ***
## category_code_LT01_5_count 0.96666 0.06246 15.476 < 2e-16 ***
## category_code_LT01_7_count 0.60750 0.15515 3.916 0.000103 ***
## category_code_LT01_9_count 0.39602 0.23061 1.717 0.086557 .
## category_code_LT01_13_count 0.13801 0.24857 0.555 0.579002
## category_code_LT01_14_count 0.20492 0.33139 0.618 0.536619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.611281389985823
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0492 -0.7810 0.0038 0.9300 3.8095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99146 0.08851 112.887 < 2e-16 ***
## category_code_LT01_2_count 0.86097 0.07214 11.935 < 2e-16 ***
## category_code_LT01_5_count 0.97081 0.06205 15.645 < 2e-16 ***
## category_code_LT01_7_count 0.61611 0.15452 3.987 7.7e-05 ***
## category_code_LT01_9_count 0.40862 0.23013 1.776 0.0764 .
## category_code_LT01_13_count 0.14922 0.24890 0.600 0.5491
## category_code_LT01_15_count 0.59345 0.75902 0.782 0.4347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6113
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610818876688156
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0496 -0.7813 0.0101 0.9542 3.8092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99177 0.08857 112.813 < 2e-16 ***
## category_code_LT01_2_count 0.86946 0.07162 12.140 < 2e-16 ***
## category_code_LT01_5_count 0.97082 0.06210 15.633 < 2e-16 ***
## category_code_LT01_7_count 0.61643 0.15464 3.986 7.73e-05 ***
## category_code_LT01_9_count 0.40403 0.23031 1.754 0.080 .
## category_code_LT01_13_count 0.13983 0.24884 0.562 0.574
## category_code_LT01_16_count 0.19558 1.18873 0.165 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.611284558405472
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0499 -0.7837 0.0097 0.9261 3.8069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99410 0.08858 112.830 < 2e-16 ***
## category_code_LT01_2_count 0.86031 0.07227 11.904 < 2e-16 ***
## category_code_LT01_5_count 0.96793 0.06241 15.510 < 2e-16 ***
## category_code_LT01_7_count 0.62099 0.15357 4.044 6.11e-05 ***
## category_code_LT01_9_count 0.38981 0.23000 1.695 0.0907 .
## category_code_LT01_14_count 0.19978 0.33139 0.603 0.5469
## category_code_LT01_15_count 0.55762 0.75800 0.736 0.4623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.404 on 491 degrees of freedom
## Multiple R-squared: 0.616, Adjusted R-squared: 0.6113
## F-statistic: 131.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610881911753246
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0501 -0.7840 0.0152 0.9271 3.8065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99449 0.08863 112.761 < 2e-16 ***
## category_code_LT01_2_count 0.86750 0.07191 12.063 < 2e-16 ***
## category_code_LT01_5_count 0.96764 0.06246 15.493 < 2e-16 ***
## category_code_LT01_7_count 0.62024 0.15367 4.036 6.3e-05 ***
## category_code_LT01_9_count 0.38550 0.23022 1.674 0.0947 .
## category_code_LT01_14_count 0.20879 0.33206 0.629 0.5298
## category_code_LT01_16_count 0.21463 1.18987 0.180 0.8569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6156, Adjusted R-squared: 0.6109
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.611020223158757
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0508 -0.7817 0.0021 0.9276 3.8088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99216 0.08854 112.850 < 2e-16 ***
## category_code_LT01_2_count 0.86471 0.07221 11.975 < 2e-16 ***
## category_code_LT01_5_count 0.97198 0.06204 15.667 < 2e-16 ***
## category_code_LT01_7_count 0.62993 0.15306 4.116 4.53e-05 ***
## category_code_LT01_9_count 0.39750 0.22971 1.730 0.0842 .
## category_code_LT01_15_count 0.57278 0.75861 0.755 0.4506
## category_code_LT01_16_count 0.20420 1.18842 0.172 0.8636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6157, Adjusted R-squared: 0.611
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.617955231028749
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0324 -0.7678 0.0140 0.9282 3.8227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97826 0.09110 109.535 < 2e-16 ***
## category_code_LT01_2_count 0.71358 0.08709 8.193 2.22e-15 ***
## category_code_LT01_5_count 0.96549 0.06171 15.645 < 2e-16 ***
## category_code_LT01_7_count 0.48708 0.15831 3.077 0.002209 **
## category_code_LT01_10_count 0.11238 0.11321 0.993 0.321362
## category_code_LT01_11_count 0.40112 0.12110 3.312 0.000994 ***
## category_code_LT01_12_count -0.05263 0.21419 -0.246 0.805998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.617970038944357
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0309 -0.7676 0.0299 0.9331 3.8229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97806 0.09109 109.538 < 2e-16 ***
## category_code_LT01_2_count 0.71107 0.08708 8.165 2.72e-15 ***
## category_code_LT01_5_count 0.96365 0.06148 15.675 < 2e-16 ***
## category_code_LT01_7_count 0.48423 0.15909 3.044 0.002462 **
## category_code_LT01_10_count 0.11076 0.11316 0.979 0.328193
## category_code_LT01_11_count 0.39222 0.11715 3.348 0.000876 ***
## category_code_LT01_13_count 0.06932 0.24598 0.282 0.778211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.618056215192389
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0329 -0.7715 0.0292 0.9087 3.8191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98189 0.09149 109.098 < 2e-16 ***
## category_code_LT01_2_count 0.70913 0.08727 8.126 3.64e-15 ***
## category_code_LT01_5_count 0.96103 0.06185 15.537 < 2e-16 ***
## category_code_LT01_7_count 0.48468 0.15837 3.060 0.002332 **
## category_code_LT01_10_count 0.10079 0.11572 0.871 0.384199
## category_code_LT01_11_count 0.39228 0.11708 3.351 0.000869 ***
## category_code_LT01_14_count 0.14629 0.33541 0.436 0.662930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6181
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.618022113663905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0321 -0.7684 0.0163 0.9364 3.8221
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97882 0.09111 109.531 < 2e-16 ***
## category_code_LT01_2_count 0.70973 0.08723 8.136 3.37e-15 ***
## category_code_LT01_5_count 0.96427 0.06145 15.692 < 2e-16 ***
## category_code_LT01_7_count 0.49188 0.15813 3.111 0.001976 **
## category_code_LT01_10_count 0.10784 0.11352 0.950 0.342614
## category_code_LT01_11_count 0.39006 0.11740 3.323 0.000958 ***
## category_code_LT01_15_count 0.28924 0.75604 0.383 0.702201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.61794002856198
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0315 -0.7681 0.0253 0.9323 3.8224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97854 0.09112 109.509 < 2e-16 ***
## category_code_LT01_2_count 0.71049 0.08747 8.123 3.72e-15 ***
## category_code_LT01_5_count 0.96387 0.06147 15.681 < 2e-16 ***
## category_code_LT01_7_count 0.49009 0.15805 3.101 0.002041 **
## category_code_LT01_10_count 0.11054 0.11323 0.976 0.329391
## category_code_LT01_11_count 0.39382 0.11708 3.364 0.000829 ***
## category_code_LT01_16_count 0.23794 1.17737 0.202 0.839929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.6179
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.609540095520371
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0308 -0.7741 0.0124 0.9280 3.8309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97006 0.09206 108.299 < 2e-16 ***
## category_code_LT01_2_count 0.87538 0.07265 12.049 < 2e-16 ***
## category_code_LT01_5_count 0.97505 0.06233 15.643 < 2e-16 ***
## category_code_LT01_7_count 0.63320 0.15433 4.103 4.78e-05 ***
## category_code_LT01_10_count 0.11918 0.11445 1.041 0.298
## category_code_LT01_12_count 0.12677 0.20940 0.605 0.545
## category_code_LT01_13_count 0.09709 0.24859 0.391 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.609592444561158
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0332 -0.7905 0.0133 0.9137 3.8267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97422 0.09248 107.854 < 2e-16 ***
## category_code_LT01_2_count 0.87432 0.07277 12.016 < 2e-16 ***
## category_code_LT01_5_count 0.97252 0.06268 15.517 < 2e-16 ***
## category_code_LT01_7_count 0.63556 0.15344 4.142 4.05e-05 ***
## category_code_LT01_10_count 0.10869 0.11700 0.929 0.353
## category_code_LT01_12_count 0.12202 0.20984 0.581 0.561
## category_code_LT01_14_count 0.15884 0.33989 0.467 0.640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609747673764904
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0326 -0.7610 0.0034 0.9263 3.8295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97141 0.09206 108.316 < 2e-16 ***
## category_code_LT01_2_count 0.87049 0.07326 11.883 < 2e-16 ***
## category_code_LT01_5_count 0.97566 0.06229 15.662 < 2e-16 ***
## category_code_LT01_7_count 0.64300 0.15296 4.204 3.12e-05 ***
## category_code_LT01_10_count 0.11381 0.11481 0.991 0.322
## category_code_LT01_12_count 0.13154 0.20929 0.628 0.530
## category_code_LT01_15_count 0.49022 0.76208 0.643 0.520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6097
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.609441023416308
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0315 -0.7753 0.0047 0.9273 3.8305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97044 0.09210 108.261 < 2e-16 ***
## category_code_LT01_2_count 0.87631 0.07297 12.009 < 2e-16 ***
## category_code_LT01_5_count 0.97549 0.06233 15.650 < 2e-16 ***
## category_code_LT01_7_count 0.64180 0.15305 4.194 3.26e-05 ***
## category_code_LT01_10_count 0.11937 0.11453 1.042 0.298
## category_code_LT01_12_count 0.12979 0.20938 0.620 0.536
## category_code_LT01_16_count 0.19907 1.19054 0.167 0.867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6094
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.609458322807038
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0355 -0.7881 0.0237 0.9233 3.8264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97458 0.09249 107.842 < 2e-16 ***
## category_code_LT01_2_count 0.88181 0.07091 12.435 < 2e-16 ***
## category_code_LT01_5_count 0.97534 0.06241 15.628 < 2e-16 ***
## category_code_LT01_7_count 0.62801 0.15483 4.056 5.8e-05 ***
## category_code_LT01_10_count 0.10929 0.11701 0.934 0.351
## category_code_LT01_13_count 0.10229 0.24853 0.412 0.681
## category_code_LT01_14_count 0.17411 0.33908 0.513 0.608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.609590846435743
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0349 -0.7693 0.0133 0.9315 3.8296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97139 0.09208 108.294 < 2e-16 ***
## category_code_LT01_2_count 0.87883 0.07128 12.330 < 2e-16 ***
## category_code_LT01_5_count 0.97905 0.06197 15.798 < 2e-16 ***
## category_code_LT01_7_count 0.63546 0.15432 4.118 4.49e-05 ***
## category_code_LT01_10_count 0.11552 0.11477 1.006 0.315
## category_code_LT01_13_count 0.11064 0.24888 0.445 0.657
## category_code_LT01_15_count 0.50080 0.76334 0.656 0.512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609271942531751
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0338 -0.7894 0.0089 0.9297 3.8305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97041 0.09212 108.237 < 2e-16 ***
## category_code_LT01_2_count 0.88482 0.07095 12.470 < 2e-16 ***
## category_code_LT01_5_count 0.97887 0.06201 15.786 < 2e-16 ***
## category_code_LT01_7_count 0.63484 0.15440 4.112 4.61e-05 ***
## category_code_LT01_10_count 0.12123 0.11448 1.059 0.290
## category_code_LT01_13_count 0.10306 0.24879 0.414 0.679
## category_code_LT01_16_count 0.20401 1.19153 0.171 0.864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6093
## F-statistic: 130.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609639080289839
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0374 -0.7654 0.0119 0.9215 3.8251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97586 0.09249 107.855 < 2e-16 ***
## category_code_LT01_2_count 0.87767 0.07142 12.288 < 2e-16 ***
## category_code_LT01_5_count 0.97618 0.06236 15.653 < 2e-16 ***
## category_code_LT01_7_count 0.63832 0.15345 4.160 3.76e-05 ***
## category_code_LT01_10_count 0.10434 0.11735 0.889 0.374
## category_code_LT01_14_count 0.17229 0.33899 0.508 0.612
## category_code_LT01_15_count 0.48006 0.76206 0.630 0.529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6096
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609351989295572
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0364 -0.7750 0.0100 0.9225 3.8259
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97509 0.09254 107.787 < 2e-16 ***
## category_code_LT01_2_count 0.88286 0.07121 12.398 < 2e-16 ***
## category_code_LT01_5_count 0.97582 0.06241 15.636 < 2e-16 ***
## category_code_LT01_7_count 0.63705 0.15351 4.150 3.92e-05 ***
## category_code_LT01_10_count 0.10927 0.11715 0.933 0.351
## category_code_LT01_14_count 0.17734 0.33988 0.522 0.602
## category_code_LT01_16_count 0.22544 1.19308 0.189 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6094
## F-statistic: 130.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609459763151016
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0357 -0.7673 -0.0034 0.9296 3.8292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97177 0.09212 108.249 < 2e-16 ***
## category_code_LT01_2_count 0.88047 0.07150 12.314 < 2e-16 ***
## category_code_LT01_5_count 0.97966 0.06197 15.810 < 2e-16 ***
## category_code_LT01_7_count 0.64522 0.15305 4.216 2.97e-05 ***
## category_code_LT01_10_count 0.11603 0.11485 1.010 0.313
## category_code_LT01_15_count 0.48718 0.76289 0.639 0.523
## category_code_LT01_16_count 0.21556 1.19129 0.181 0.856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617261533518011
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0568 -0.7634 0.0164 0.9134 3.7985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00243 0.08781 113.911 < 2e-16 ***
## category_code_LT01_2_count 0.72211 0.08672 8.327 8.32e-16 ***
## category_code_LT01_5_count 0.96586 0.06178 15.633 < 2e-16 ***
## category_code_LT01_7_count 0.49367 0.15909 3.103 0.002025 **
## category_code_LT01_11_count 0.40237 0.12125 3.318 0.000973 ***
## category_code_LT01_12_count -0.04657 0.21430 -0.217 0.828036
## category_code_LT01_13_count 0.07536 0.24620 0.306 0.759678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617518339838599
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0565 -0.7839 0.0096 0.9101 3.7961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00485 0.08784 113.893 < 2e-16 ***
## category_code_LT01_2_count 0.71764 0.08704 8.245 1.52e-15 ***
## category_code_LT01_5_count 0.96196 0.06211 15.488 < 2e-16 ***
## category_code_LT01_7_count 0.49039 0.15852 3.093 0.00209 **
## category_code_LT01_11_count 0.40283 0.12115 3.325 0.00095 ***
## category_code_LT01_12_count -0.05560 0.21477 -0.259 0.79582
## category_code_LT01_14_count 0.21409 0.32902 0.651 0.51554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617349669523134
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0573 -0.7539 -0.0046 0.9199 3.7984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00252 0.08780 113.928 < 2e-16 ***
## category_code_LT01_2_count 0.71995 0.08695 8.280 1.17e-15 ***
## category_code_LT01_5_count 0.96640 0.06176 15.649 < 2e-16 ***
## category_code_LT01_7_count 0.50205 0.15805 3.177 0.00158 **
## category_code_LT01_11_count 0.39887 0.12162 3.280 0.00111 **
## category_code_LT01_12_count -0.04175 0.21437 -0.195 0.84565
## category_code_LT01_15_count 0.34316 0.75463 0.455 0.64949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.61723190360036
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0574 -0.7592 0.0158 0.9199 3.7981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00290 0.08782 113.905 < 2e-16 ***
## category_code_LT01_2_count 0.72125 0.08715 8.276 1.22e-15 ***
## category_code_LT01_5_count 0.96603 0.06178 15.637 < 2e-16 ***
## category_code_LT01_7_count 0.50015 0.15799 3.166 0.001644 **
## category_code_LT01_11_count 0.40383 0.12120 3.332 0.000927 ***
## category_code_LT01_12_count -0.04447 0.21429 -0.208 0.835686
## category_code_LT01_16_count 0.27788 1.17776 0.236 0.813581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617538361124319
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0546 -0.7938 0.0186 0.9097 3.7967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00421 0.08783 113.907 < 2e-16 ***
## category_code_LT01_2_count 0.71497 0.08706 8.212 1.93e-15 ***
## category_code_LT01_5_count 0.96010 0.06192 15.506 < 2e-16 ***
## category_code_LT01_7_count 0.48730 0.15930 3.059 0.002343 **
## category_code_LT01_11_count 0.39340 0.11722 3.356 0.000852 ***
## category_code_LT01_13_count 0.07493 0.24607 0.305 0.760855
## category_code_LT01_14_count 0.20821 0.32811 0.635 0.526015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.617404692425833
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0556 -0.7751 -0.0001 0.9167 3.7989
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00201 0.08778 113.943 < 2e-16 ***
## category_code_LT01_2_count 0.71711 0.08693 8.249 1.48e-15 ***
## category_code_LT01_5_count 0.96475 0.06151 15.683 < 2e-16 ***
## category_code_LT01_7_count 0.49780 0.15883 3.134 0.001827 **
## category_code_LT01_11_count 0.39103 0.11757 3.326 0.000947 ***
## category_code_LT01_13_count 0.08123 0.24653 0.329 0.741938
## category_code_LT01_15_count 0.36302 0.75536 0.481 0.631016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.61727401339032
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0557 -0.7822 0.0175 0.9175 3.7986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00241 0.08780 113.918 < 2e-16 ***
## category_code_LT01_2_count 0.71848 0.08715 8.245 1.53e-15 ***
## category_code_LT01_5_count 0.96431 0.06154 15.670 < 2e-16 ***
## category_code_LT01_7_count 0.49625 0.15881 3.125 0.001885 **
## category_code_LT01_11_count 0.39594 0.11721 3.378 0.000788 ***
## category_code_LT01_13_count 0.07676 0.24635 0.312 0.755477
## category_code_LT01_16_count 0.29637 1.17845 0.251 0.801540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.617624037850081
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0552 -0.7644 0.0070 0.9133 3.7967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00430 0.08782 113.924 < 2e-16 ***
## category_code_LT01_2_count 0.71304 0.08724 8.173 2.58e-15 ***
## category_code_LT01_5_count 0.96083 0.06189 15.525 < 2e-16 ***
## category_code_LT01_7_count 0.49555 0.15827 3.131 0.001846 **
## category_code_LT01_11_count 0.39067 0.11747 3.326 0.000948 ***
## category_code_LT01_14_count 0.20499 0.32813 0.625 0.532447
## category_code_LT01_15_count 0.33953 0.75399 0.450 0.652691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617525433968892
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0552 -0.7665 0.0142 0.9144 3.7962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00479 0.08784 113.901 < 2e-16 ***
## category_code_LT01_2_count 0.71361 0.08754 8.152 3e-15 ***
## category_code_LT01_5_count 0.96017 0.06192 15.507 < 2e-16 ***
## category_code_LT01_7_count 0.49357 0.15821 3.120 0.001916 **
## category_code_LT01_11_count 0.39516 0.11713 3.374 0.000801 ***
## category_code_LT01_14_count 0.21297 0.32865 0.648 0.517281
## category_code_LT01_16_count 0.32534 1.17906 0.276 0.782717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617371525796659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0563 -0.7629 -0.0004 0.9215 3.7984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00254 0.08779 113.937 < 2e-16 ***
## category_code_LT01_2_count 0.71628 0.08736 8.199 2.12e-15 ***
## category_code_LT01_5_count 0.96498 0.06151 15.689 < 2e-16 ***
## category_code_LT01_7_count 0.50464 0.15779 3.198 0.001472 **
## category_code_LT01_11_count 0.39303 0.11747 3.346 0.000883 ***
## category_code_LT01_15_count 0.35576 0.75463 0.471 0.637540
## category_code_LT01_16_count 0.30265 1.17821 0.257 0.797385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.609043686443509
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0564 -0.8074 -0.0036 0.9094 3.8027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99821 0.08879 112.602 < 2e-16 ***
## category_code_LT01_2_count 0.88031 0.07251 12.141 < 2e-16 ***
## category_code_LT01_5_count 0.97133 0.06274 15.482 < 2e-16 ***
## category_code_LT01_7_count 0.63695 0.15457 4.121 4.43e-05 ***
## category_code_LT01_12_count 0.12437 0.21001 0.592 0.554
## category_code_LT01_13_count 0.10333 0.24870 0.415 0.678
## category_code_LT01_14_count 0.22551 0.33263 0.678 0.498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.609
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.609129746595399
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0570 -0.7853 -0.0147 0.9167 3.8052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99577 0.08871 112.674 < 2e-16 ***
## category_code_LT01_2_count 0.87733 0.07296 12.025 < 2e-16 ***
## category_code_LT01_5_count 0.97580 0.06236 15.648 < 2e-16 ***
## category_code_LT01_7_count 0.64732 0.15385 4.207 3.07e-05 ***
## category_code_LT01_12_count 0.13770 0.20935 0.658 0.511
## category_code_LT01_13_count 0.11271 0.24904 0.453 0.651
## category_code_LT01_15_count 0.57347 0.76103 0.754 0.451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.6091
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.608717646027678
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0573 -0.7857 -0.0129 0.9191 3.8049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99610 0.08877 112.609 < 2e-16 ***
## category_code_LT01_2_count 0.88472 0.07255 12.194 < 2e-16 ***
## category_code_LT01_5_count 0.97567 0.06241 15.634 < 2e-16 ***
## category_code_LT01_7_count 0.64745 0.15395 4.206 3.1e-05 ***
## category_code_LT01_12_count 0.13645 0.20948 0.651 0.515
## category_code_LT01_13_count 0.10467 0.24899 0.420 0.674
## category_code_LT01_16_count 0.26676 1.19167 0.224 0.823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6134, Adjusted R-squared: 0.6087
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.60931303913143
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0570 -0.7879 -0.0053 0.9042 3.8026
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99834 0.08876 112.645 < 2e-16 ***
## category_code_LT01_2_count 0.87437 0.07320 11.946 < 2e-16 ***
## category_code_LT01_5_count 0.97205 0.06270 15.504 < 2e-16 ***
## category_code_LT01_7_count 0.64702 0.15313 4.225 2.84e-05 ***
## category_code_LT01_12_count 0.12940 0.20988 0.617 0.538
## category_code_LT01_14_count 0.21943 0.33260 0.660 0.510
## category_code_LT01_15_count 0.54335 0.75991 0.715 0.475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.614, Adjusted R-squared: 0.6093
## F-statistic: 130.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608954314271746
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0571 -0.7883 -0.0086 0.9085 3.8022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99877 0.08881 112.585 < 2e-16 ***
## category_code_LT01_2_count 0.88055 0.07293 12.074 < 2e-16 ***
## category_code_LT01_5_count 0.97163 0.06275 15.485 < 2e-16 ***
## category_code_LT01_7_count 0.64618 0.15320 4.218 2.94e-05 ***
## category_code_LT01_12_count 0.12767 0.20997 0.608 0.543
## category_code_LT01_14_count 0.22938 0.33318 0.688 0.491
## category_code_LT01_16_count 0.29292 1.19225 0.246 0.806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.609
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609010975551222
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0579 -0.7823 -0.0189 0.9127 3.8046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99631 0.08873 112.656 < 2e-16 ***
## category_code_LT01_2_count 0.87822 0.07331 11.979 < 2e-16 ***
## category_code_LT01_5_count 0.97625 0.06236 15.655 < 2e-16 ***
## category_code_LT01_7_count 0.65745 0.15252 4.310 1.97e-05 ***
## category_code_LT01_12_count 0.14134 0.20934 0.675 0.500
## category_code_LT01_15_count 0.56159 0.76058 0.738 0.461
## category_code_LT01_16_count 0.28094 1.19119 0.236 0.814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.609
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609188274821804
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0595 -0.7884 -0.0082 0.9171 3.8021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99884 0.08877 112.641 < 2e-16 ***
## category_code_LT01_2_count 0.88192 0.07133 12.363 < 2e-16 ***
## category_code_LT01_5_count 0.97498 0.06243 15.617 < 2e-16 ***
## category_code_LT01_7_count 0.63834 0.15454 4.131 4.25e-05 ***
## category_code_LT01_13_count 0.11762 0.24892 0.473 0.637
## category_code_LT01_14_count 0.23593 0.33160 0.711 0.477
## category_code_LT01_15_count 0.55537 0.76108 0.730 0.466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6092
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608816014065912
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0597 -0.7889 -0.0037 0.9188 3.8017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99929 0.08882 112.580 < 2e-16 ***
## category_code_LT01_2_count 0.88826 0.07104 12.503 < 2e-16 ***
## category_code_LT01_5_count 0.97454 0.06248 15.597 < 2e-16 ***
## category_code_LT01_7_count 0.63810 0.15462 4.127 4.32e-05 ***
## category_code_LT01_13_count 0.11018 0.24887 0.443 0.658
## category_code_LT01_14_count 0.24592 0.33223 0.740 0.460
## category_code_LT01_16_count 0.30359 1.19330 0.254 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608832269817539
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0608 -0.7694 -0.0230 0.9294 3.8043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99670 0.08875 112.639 < 2e-16 ***
## category_code_LT01_2_count 0.88726 0.07124 12.455 < 2e-16 ***
## category_code_LT01_5_count 0.97990 0.06203 15.796 < 2e-16 ***
## category_code_LT01_7_count 0.64951 0.15391 4.220 2.91e-05 ***
## category_code_LT01_13_count 0.11988 0.24926 0.481 0.631
## category_code_LT01_15_count 0.57436 0.76188 0.754 0.451
## category_code_LT01_16_count 0.28935 1.19229 0.243 0.808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609065740190534
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0605 -0.7890 -0.0155 0.9160 3.8015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99948 0.08879 112.620 < 2e-16 ***
## category_code_LT01_2_count 0.88286 0.07167 12.318 < 2e-16 ***
## category_code_LT01_5_count 0.97545 0.06243 15.625 < 2e-16 ***
## category_code_LT01_7_count 0.64881 0.15319 4.235 2.72e-05 ***
## category_code_LT01_14_count 0.24062 0.33219 0.724 0.469
## category_code_LT01_15_count 0.54304 0.76057 0.714 0.476
## category_code_LT01_16_count 0.31398 1.19278 0.263 0.792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.6091
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.612832734480904
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0351 -0.7713 0.0313 0.9179 4.3761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97650 0.09173 108.761 < 2e-16 ***
## category_code_LT01_2_count 0.72656 0.08769 8.286 1.13e-15 ***
## category_code_LT01_5_count 0.97194 0.06268 15.507 < 2e-16 ***
## category_code_LT01_8_count -0.08426 0.27755 -0.304 0.7616
## category_code_LT01_9_count 0.39665 0.23013 1.724 0.0854 .
## category_code_LT01_10_count 0.11580 0.11440 1.012 0.3119
## category_code_LT01_11_count 0.48337 0.11315 4.272 2.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.6128
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.612129585261763
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0621 -0.7914 0.0173 0.8967 4.3271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00181 0.08848 113.044 < 2e-16 ***
## category_code_LT01_2_count 0.73798 0.08736 8.448 3.39e-16 ***
## category_code_LT01_5_count 0.97435 0.06293 15.482 < 2e-16 ***
## category_code_LT01_8_count -0.07366 0.27800 -0.265 0.7911
## category_code_LT01_9_count 0.42326 0.22874 1.850 0.0649 .
## category_code_LT01_11_count 0.49940 0.11691 4.272 2.33e-05 ***
## category_code_LT01_12_count -0.07849 0.21554 -0.364 0.7159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.612495916370184
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0578 -0.7900 0.0309 0.9099 4.3436
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00046 0.08843 113.085 < 2e-16 ***
## category_code_LT01_2_count 0.73067 0.08750 8.350 7.00e-16 ***
## category_code_LT01_5_count 0.97015 0.06278 15.454 < 2e-16 ***
## category_code_LT01_8_count -0.06698 0.27798 -0.241 0.8097
## category_code_LT01_9_count 0.43473 0.22903 1.898 0.0583 .
## category_code_LT01_11_count 0.48156 0.11345 4.245 2.62e-05 ***
## category_code_LT01_13_count 0.19064 0.24676 0.773 0.4401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6125
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.612492611915638
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0592 -0.7936 0.0226 0.8991 4.3437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00402 0.08849 113.047 < 2e-16 ***
## category_code_LT01_2_count 0.72892 0.08772 8.309 9.47e-16 ***
## category_code_LT01_5_count 0.96710 0.06310 15.327 < 2e-16 ***
## category_code_LT01_8_count -0.08161 0.27764 -0.294 0.7689
## category_code_LT01_9_count 0.41203 0.22916 1.798 0.0728 .
## category_code_LT01_11_count 0.48421 0.11323 4.276 2.28e-05 ***
## category_code_LT01_14_count 0.25402 0.32995 0.770 0.4417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6125
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.612149960244381
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0604 -0.7908 0.0095 0.8982 4.3375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00127 0.08847 113.053 < 2e-16 ***
## category_code_LT01_2_count 0.73323 0.08758 8.372 5.94e-16 ***
## category_code_LT01_5_count 0.97272 0.06273 15.506 < 2e-16 ***
## category_code_LT01_8_count -0.07985 0.27776 -0.287 0.774
## category_code_LT01_9_count 0.42629 0.22879 1.863 0.063 .
## category_code_LT01_11_count 0.48533 0.11344 4.278 2.27e-05 ***
## category_code_LT01_15_count 0.30211 0.75908 0.398 0.691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.612041002757731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0605 -0.7911 0.0246 0.9003 4.3335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00154 0.08849 113.025 < 2e-16 ***
## category_code_LT01_2_count 0.73499 0.08773 8.378 5.72e-16 ***
## category_code_LT01_5_count 0.97244 0.06274 15.499 < 2e-16 ***
## category_code_LT01_8_count -0.08009 0.27810 -0.288 0.773
## category_code_LT01_9_count 0.42327 0.22884 1.850 0.065 .
## category_code_LT01_11_count 0.48899 0.11317 4.321 1.88e-05 ***
## category_code_LT01_16_count 0.16983 1.18731 0.143 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.612
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.610629353619806
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0423 -0.7582 0.0135 0.8989 4.3276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97655 0.09199 108.452 < 2e-16 ***
## category_code_LT01_2_count 0.74985 0.08714 8.605 < 2e-16 ***
## category_code_LT01_5_count 0.98235 0.06287 15.626 < 2e-16 ***
## category_code_LT01_8_count -0.06252 0.27842 -0.225 0.822
## category_code_LT01_10_count 0.14046 0.11397 1.232 0.218
## category_code_LT01_11_count 0.51005 0.11689 4.364 1.56e-05 ***
## category_code_LT01_12_count -0.09046 0.21604 -0.419 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.61079629799995
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0394 -0.7639 0.0248 0.9097 4.3395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97619 0.09197 108.473 < 2e-16 ***
## category_code_LT01_2_count 0.74431 0.08724 8.532 < 2e-16 ***
## category_code_LT01_5_count 0.97857 0.06271 15.604 < 2e-16 ***
## category_code_LT01_8_count -0.05803 0.27853 -0.208 0.835
## category_code_LT01_10_count 0.13689 0.11395 1.201 0.230
## category_code_LT01_11_count 0.49253 0.11346 4.341 1.72e-05 ***
## category_code_LT01_13_count 0.15345 0.24697 0.621 0.535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.610820971445927
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0431 -0.7673 0.0252 0.9108 4.3369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98201 0.09239 108.043 < 2e-16 ***
## category_code_LT01_2_count 0.74275 0.08742 8.497 2.35e-16 ***
## category_code_LT01_5_count 0.97555 0.06308 15.466 < 2e-16 ***
## category_code_LT01_8_count -0.06997 0.27812 -0.252 0.801
## category_code_LT01_10_count 0.12287 0.11663 1.053 0.293
## category_code_LT01_11_count 0.49445 0.11325 4.366 1.55e-05 ***
## category_code_LT01_14_count 0.21818 0.33780 0.646 0.519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.610543072058846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0413 -0.7664 0.0112 0.9047 4.3345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97679 0.09202 108.422 < 2e-16 ***
## category_code_LT01_2_count 0.74643 0.08730 8.551 < 2e-16 ***
## category_code_LT01_5_count 0.98045 0.06268 15.642 < 2e-16 ***
## category_code_LT01_8_count -0.06845 0.27822 -0.246 0.806
## category_code_LT01_10_count 0.13675 0.11429 1.196 0.232
## category_code_LT01_11_count 0.49581 0.11344 4.371 1.51e-05 ***
## category_code_LT01_15_count 0.19676 0.76285 0.258 0.797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6105
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.610508157219036
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0409 -0.7639 0.0119 0.9051 4.3340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97665 0.09203 108.406 < 2e-16 ***
## category_code_LT01_2_count 0.74674 0.08754 8.530 < 2e-16 ***
## category_code_LT01_5_count 0.98017 0.06268 15.637 < 2e-16 ***
## category_code_LT01_8_count -0.06960 0.27854 -0.250 0.803
## category_code_LT01_10_count 0.13848 0.11401 1.215 0.225
## category_code_LT01_11_count 0.49813 0.11320 4.400 1.33e-05 ***
## category_code_LT01_16_count 0.17851 1.18996 0.150 0.881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6105
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.609775139679812
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0718 -0.7729 0.0073 0.8944 4.2782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00640 0.08871 112.805 < 2e-16 ***
## category_code_LT01_2_count 0.75926 0.08676 8.751 < 2e-16 ***
## category_code_LT01_5_count 0.98180 0.06298 15.588 < 2e-16 ***
## category_code_LT01_8_count -0.04386 0.27906 -0.157 0.875
## category_code_LT01_11_count 0.51127 0.11718 4.363 1.56e-05 ***
## category_code_LT01_12_count -0.08494 0.21621 -0.393 0.695
## category_code_LT01_13_count 0.16411 0.24721 0.664 0.507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.610098332631602
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0723 -0.7800 -0.0035 0.8932 4.2853
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01015 0.08873 112.818 < 2e-16 ***
## category_code_LT01_2_count 0.75406 0.08709 8.658 < 2e-16 ***
## category_code_LT01_5_count 0.97709 0.06330 15.437 < 2e-16 ***
## category_code_LT01_8_count -0.05807 0.27856 -0.208 0.835
## category_code_LT01_11_count 0.51267 0.11692 4.385 1.42e-05 ***
## category_code_LT01_12_count -0.09632 0.21659 -0.445 0.657
## category_code_LT01_14_count 0.30477 0.33094 0.921 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609516929851317
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0737 -0.7727 -0.0005 0.8904 4.2741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00698 0.08873 112.781 < 2e-16 ***
## category_code_LT01_2_count 0.76094 0.08688 8.758 < 2e-16 ***
## category_code_LT01_5_count 0.98375 0.06295 15.628 < 2e-16 ***
## category_code_LT01_8_count -0.05553 0.27877 -0.199 0.842
## category_code_LT01_11_count 0.51360 0.11729 4.379 1.46e-05 ***
## category_code_LT01_12_count -0.08023 0.21639 -0.371 0.711
## category_code_LT01_15_count 0.25923 0.76191 0.340 0.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.609452484708569
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0738 -0.7729 0.0064 0.8905 4.2722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00728 0.08875 112.762 < 2e-16 ***
## category_code_LT01_2_count 0.76172 0.08710 8.745 < 2e-16 ***
## category_code_LT01_5_count 0.98345 0.06296 15.621 < 2e-16 ***
## category_code_LT01_8_count -0.05664 0.27912 -0.203 0.839
## category_code_LT01_11_count 0.51702 0.11695 4.421 1.21e-05 ***
## category_code_LT01_12_count -0.08210 0.21631 -0.380 0.704
## category_code_LT01_16_count 0.22192 1.19100 0.186 0.852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.610279035892712
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0684 -0.7850 0.0127 0.8984 4.2988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00884 0.08870 112.839 < 2e-16 ***
## category_code_LT01_2_count 0.74821 0.08723 8.577 < 2e-16 ***
## category_code_LT01_5_count 0.97331 0.06318 15.406 < 2e-16 ***
## category_code_LT01_8_count -0.05349 0.27866 -0.192 0.848
## category_code_LT01_11_count 0.49411 0.11355 4.351 1.65e-05 ***
## category_code_LT01_13_count 0.16114 0.24702 0.652 0.515
## category_code_LT01_14_count 0.29330 0.33012 0.888 0.375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.615, Adjusted R-squared: 0.6103
## F-statistic: 130.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.609776204439835
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0700 -0.7855 0.0032 0.8926 4.2888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00584 0.08870 112.810 < 2e-16 ***
## category_code_LT01_2_count 0.75445 0.08701 8.671 < 2e-16 ***
## category_code_LT01_5_count 0.98003 0.06279 15.608 < 2e-16 ***
## category_code_LT01_8_count -0.04998 0.27880 -0.179 0.858
## category_code_LT01_11_count 0.49630 0.11378 4.362 1.57e-05 ***
## category_code_LT01_13_count 0.16865 0.24764 0.681 0.496
## category_code_LT01_15_count 0.30090 0.76262 0.395 0.693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609690494288763
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0701 -0.7787 0.0144 0.8953 4.2865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00620 0.08872 112.789 < 2e-16 ***
## category_code_LT01_2_count 0.75539 0.08722 8.660 < 2e-16 ***
## category_code_LT01_5_count 0.97968 0.06280 15.600 < 2e-16 ***
## category_code_LT01_8_count -0.05167 0.27913 -0.185 0.853
## category_code_LT01_11_count 0.50012 0.11347 4.407 1.29e-05 ***
## category_code_LT01_13_count 0.16476 0.24739 0.666 0.506
## category_code_LT01_16_count 0.26051 1.19134 0.219 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6097
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610032840563647
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0704 -0.7637 0.0023 0.8884 4.2946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00943 0.08872 112.817 < 2e-16 ***
## category_code_LT01_2_count 0.74994 0.08732 8.588 < 2e-16 ***
## category_code_LT01_5_count 0.97533 0.06314 15.446 < 2e-16 ***
## category_code_LT01_8_count -0.06470 0.27836 -0.232 0.816
## category_code_LT01_11_count 0.49699 0.11355 4.377 1.47e-05 ***
## category_code_LT01_14_count 0.29302 0.33025 0.887 0.375
## category_code_LT01_15_count 0.25839 0.76104 0.340 0.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.61
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609989510930993
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0704 -0.7642 0.0045 0.8975 4.2943
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00987 0.08874 112.798 < 2e-16 ***
## category_code_LT01_2_count 0.74987 0.08763 8.557 < 2e-16 ***
## category_code_LT01_5_count 0.97480 0.06315 15.436 < 2e-16 ***
## category_code_LT01_8_count -0.06687 0.27871 -0.240 0.810
## category_code_LT01_11_count 0.50014 0.11328 4.415 1.24e-05 ***
## category_code_LT01_14_count 0.29962 0.33081 0.906 0.366
## category_code_LT01_16_count 0.29372 1.19209 0.246 0.805
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.61
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609441500743491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0722 -0.7813 0.0010 0.8906 4.2822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00679 0.08874 112.766 < 2e-16 ***
## category_code_LT01_2_count 0.75715 0.08733 8.670 < 2e-16 ***
## category_code_LT01_5_count 0.98176 0.06276 15.643 < 2e-16 ***
## category_code_LT01_8_count -0.06303 0.27888 -0.226 0.821
## category_code_LT01_11_count 0.50291 0.11347 4.432 1.15e-05 ***
## category_code_LT01_15_count 0.27502 0.76206 0.361 0.718
## category_code_LT01_16_count 0.24600 1.19168 0.206 0.837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6094
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.612891409882819
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0341 -0.7750 0.0188 0.9136 4.3735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97607 0.09170 108.796 < 2e-16 ***
## category_code_LT01_2_count 0.72802 0.08778 8.293 1.07e-15 ***
## category_code_LT01_5_count 0.97122 0.06216 15.626 < 2e-16 ***
## category_code_LT01_9_count 0.39300 0.22999 1.709 0.0881 .
## category_code_LT01_10_count 0.11650 0.11442 1.018 0.3091
## category_code_LT01_11_count 0.49576 0.11684 4.243 2.64e-05 ***
## category_code_LT01_12_count -0.08785 0.21523 -0.408 0.6833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6176, Adjusted R-squared: 0.6129
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.613203056166949
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7871 0.0343 0.9257 4.3882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97575 0.09166 108.839 < 2e-16 ***
## category_code_LT01_2_count 0.72110 0.08791 8.203 2.08e-15 ***
## category_code_LT01_5_count 0.96710 0.06194 15.613 < 2e-16 ***
## category_code_LT01_9_count 0.40555 0.23038 1.760 0.079 .
## category_code_LT01_10_count 0.11184 0.11440 0.978 0.329
## category_code_LT01_11_count 0.47704 0.11345 4.205 3.11e-05 ***
## category_code_LT01_13_count 0.18475 0.24637 0.750 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6179, Adjusted R-squared: 0.6132
## F-statistic: 132.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.613014003066555
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0345 -0.7703 0.0306 0.9195 4.3816
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98079 0.09210 108.372 < 2e-16 ***
## category_code_LT01_2_count 0.72185 0.08801 8.202 2.08e-15 ***
## category_code_LT01_5_count 0.96491 0.06232 15.483 < 2e-16 ***
## category_code_LT01_9_count 0.38831 0.23017 1.687 0.0922 .
## category_code_LT01_10_count 0.10114 0.11694 0.865 0.3875
## category_code_LT01_11_count 0.48113 0.11322 4.250 2.56e-05 ***
## category_code_LT01_14_count 0.19138 0.33716 0.568 0.5706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6177, Adjusted R-squared: 0.613
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.612836625164534
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0330 -0.7693 0.0082 0.9222 4.3814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97638 0.09172 108.773 < 2e-16 ***
## category_code_LT01_2_count 0.72408 0.08796 8.232 1.67e-15 ***
## category_code_LT01_5_count 0.96913 0.06192 15.651 < 2e-16 ***
## category_code_LT01_9_count 0.39654 0.23011 1.723 0.0855 .
## category_code_LT01_10_count 0.11207 0.11477 0.977 0.3293
## category_code_LT01_11_count 0.48136 0.11342 4.244 2.63e-05 ***
## category_code_LT01_15_count 0.23711 0.76094 0.312 0.7555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.6128
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.612767259787679
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0325 -0.7702 0.0258 0.9192 4.3790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97601 0.09173 108.752 < 2e-16 ***
## category_code_LT01_2_count 0.72540 0.08815 8.229 1.71e-15 ***
## category_code_LT01_5_count 0.96890 0.06193 15.644 < 2e-16 ***
## category_code_LT01_9_count 0.39366 0.23008 1.711 0.0877 .
## category_code_LT01_10_count 0.11467 0.11445 1.002 0.3168
## category_code_LT01_11_count 0.48407 0.11318 4.277 2.28e-05 ***
## category_code_LT01_16_count 0.11324 1.18544 0.096 0.9239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6128
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.612567559925689
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0575 -0.7899 0.0282 0.9067 4.3409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00031 0.08839 113.140 < 2e-16 ***
## category_code_LT01_2_count 0.73203 0.08760 8.357 6.67e-16 ***
## category_code_LT01_5_count 0.96987 0.06223 15.586 < 2e-16 ***
## category_code_LT01_9_count 0.43208 0.22888 1.888 0.0596 .
## category_code_LT01_11_count 0.49307 0.11715 4.209 3.05e-05 ***
## category_code_LT01_12_count -0.08304 0.21523 -0.386 0.6998
## category_code_LT01_13_count 0.19486 0.24642 0.791 0.4295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6126
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.612570675029116
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0586 -0.7934 0.0237 0.8990 4.3409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00388 0.08845 113.097 < 2e-16 ***
## category_code_LT01_2_count 0.73032 0.08780 8.318 8.88e-16 ***
## category_code_LT01_5_count 0.96642 0.06255 15.450 < 2e-16 ***
## category_code_LT01_9_count 0.40819 0.22901 1.782 0.0753 .
## category_code_LT01_11_count 0.49716 0.11687 4.254 2.52e-05 ***
## category_code_LT01_12_count -0.09288 0.21573 -0.431 0.6670
## category_code_LT01_14_count 0.26231 0.33067 0.793 0.4280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6126
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.61218869486427
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0595 -0.7905 0.0112 0.8975 4.3347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00098 0.08843 113.099 < 2e-16 ***
## category_code_LT01_2_count 0.73469 0.08770 8.377 5.74e-16 ***
## category_code_LT01_5_count 0.97190 0.06221 15.623 < 2e-16 ***
## category_code_LT01_9_count 0.42295 0.22862 1.850 0.0649 .
## category_code_LT01_11_count 0.49655 0.11725 4.235 2.73e-05 ***
## category_code_LT01_12_count -0.07818 0.21544 -0.363 0.7168
## category_code_LT01_15_count 0.28923 0.75941 0.381 0.7035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.6122
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.61208616758905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0596 -0.7908 0.0149 0.9019 4.3306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00123 0.08845 113.075 < 2e-16 ***
## category_code_LT01_2_count 0.73656 0.08786 8.384 5.46e-16 ***
## category_code_LT01_5_count 0.97170 0.06223 15.615 < 2e-16 ***
## category_code_LT01_9_count 0.42012 0.22869 1.837 0.0668 .
## category_code_LT01_11_count 0.50035 0.11690 4.280 2.25e-05 ***
## category_code_LT01_12_count -0.08061 0.21537 -0.374 0.7083
## category_code_LT01_16_count 0.14641 1.18595 0.123 0.9018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.612902558846595
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0543 -0.8058 0.0350 0.9253 4.3575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00237 0.08841 113.141 < 2e-16 ***
## category_code_LT01_2_count 0.72305 0.08796 8.220 1.82e-15 ***
## category_code_LT01_5_count 0.96235 0.06238 15.428 < 2e-16 ***
## category_code_LT01_9_count 0.42068 0.22933 1.834 0.0672 .
## category_code_LT01_11_count 0.47749 0.11353 4.206 3.09e-05 ***
## category_code_LT01_13_count 0.19181 0.24631 0.779 0.4365
## category_code_LT01_14_count 0.24981 0.32975 0.758 0.4491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6176, Adjusted R-squared: 0.6129
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.612606058014336
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0554 -0.7892 0.0187 0.9211 4.3528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99964 0.08837 113.152 < 2e-16 ***
## category_code_LT01_2_count 0.72662 0.08786 8.270 1.26e-15 ***
## category_code_LT01_5_count 0.96786 0.06199 15.614 < 2e-16 ***
## category_code_LT01_9_count 0.43554 0.22894 1.902 0.0577 .
## category_code_LT01_11_count 0.47780 0.11379 4.199 3.18e-05 ***
## category_code_LT01_13_count 0.20069 0.24688 0.813 0.4167
## category_code_LT01_15_count 0.33793 0.76008 0.445 0.6568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6173, Adjusted R-squared: 0.6126
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.612470396760255
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0555 -0.7895 0.0230 0.9162 4.3481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99996 0.08840 113.125 < 2e-16 ***
## category_code_LT01_2_count 0.72875 0.08801 8.280 1.17e-15 ***
## category_code_LT01_5_count 0.96759 0.06201 15.604 < 2e-16 ***
## category_code_LT01_9_count 0.43184 0.22898 1.886 0.0599 .
## category_code_LT01_11_count 0.48210 0.11347 4.249 2.57e-05 ***
## category_code_LT01_13_count 0.19529 0.24662 0.792 0.4288
## category_code_LT01_16_count 0.19024 1.18616 0.160 0.8726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6125
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.612538525363553
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0565 -0.7926 0.0167 0.9118 4.3513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00306 0.08844 113.103 < 2e-16 ***
## category_code_LT01_2_count 0.72573 0.08804 8.243 1.54e-15 ***
## category_code_LT01_5_count 0.96446 0.06236 15.467 < 2e-16 ***
## category_code_LT01_9_count 0.41165 0.22906 1.797 0.0729 .
## category_code_LT01_11_count 0.48152 0.11351 4.242 2.65e-05 ***
## category_code_LT01_14_count 0.25021 0.32994 0.758 0.4486
## category_code_LT01_15_count 0.28851 0.75873 0.380 0.7039
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6125
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.612448774240915
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0565 -0.7930 0.0255 0.9032 4.3485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00343 0.08847 113.078 < 2e-16 ***
## category_code_LT01_2_count 0.72688 0.08827 8.234 1.65e-15 ***
## category_code_LT01_5_count 0.96402 0.06238 15.454 < 2e-16 ***
## category_code_LT01_9_count 0.40823 0.22915 1.781 0.0755 .
## category_code_LT01_11_count 0.48500 0.11324 4.283 2.22e-05 ***
## category_code_LT01_14_count 0.25595 0.33053 0.774 0.4391
## category_code_LT01_16_count 0.20857 1.18742 0.176 0.8606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6124
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.612100934657905
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0577 -0.7902 0.0057 0.9035 4.3416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00064 0.08843 113.085 < 2e-16 ***
## category_code_LT01_2_count 0.73154 0.08810 8.304 9.86e-16 ***
## category_code_LT01_5_count 0.96977 0.06198 15.646 < 2e-16 ***
## category_code_LT01_9_count 0.42285 0.22873 1.849 0.0651 .
## category_code_LT01_11_count 0.48606 0.11346 4.284 2.21e-05 ***
## category_code_LT01_15_count 0.30268 0.75954 0.399 0.6904
## category_code_LT01_16_count 0.17019 1.18657 0.143 0.8860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.610912755306223
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0393 -0.7656 0.0240 0.8951 4.3368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97600 0.09193 108.518 < 2e-16 ***
## category_code_LT01_2_count 0.74572 0.08730 8.542 < 2e-16 ***
## category_code_LT01_5_count 0.97882 0.06213 15.755 < 2e-16 ***
## category_code_LT01_10_count 0.13769 0.11394 1.208 0.227
## category_code_LT01_11_count 0.50538 0.11710 4.316 1.93e-05 ***
## category_code_LT01_12_count -0.09415 0.21577 -0.436 0.663
## category_code_LT01_13_count 0.15752 0.24658 0.639 0.523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6156, Adjusted R-squared: 0.6109
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.610946439922333
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0430 -0.7694 0.0214 0.8985 4.3341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98198 0.09235 108.088 < 2e-16 ***
## category_code_LT01_2_count 0.74413 0.08747 8.508 < 2e-16 ***
## category_code_LT01_5_count 0.97541 0.06249 15.608 < 2e-16 ***
## category_code_LT01_10_count 0.12306 0.11659 1.055 0.292
## category_code_LT01_11_count 0.50839 0.11686 4.350 1.65e-05 ***
## category_code_LT01_12_count -0.10178 0.21619 -0.471 0.638
## category_code_LT01_14_count 0.22717 0.33840 0.671 0.502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6156, Adjusted R-squared: 0.6109
## F-statistic: 131.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.610634764309909
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0408 -0.7660 0.0119 0.8881 4.3317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97647 0.09198 108.464 < 2e-16 ***
## category_code_LT01_2_count 0.74795 0.08739 8.559 < 2e-16 ***
## category_code_LT01_5_count 0.98027 0.06212 15.781 < 2e-16 ***
## category_code_LT01_10_count 0.13762 0.11431 1.204 0.229
## category_code_LT01_11_count 0.50851 0.11719 4.339 1.74e-05 ***
## category_code_LT01_12_count -0.09065 0.21598 -0.420 0.675
## category_code_LT01_15_count 0.18261 0.76321 0.239 0.811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610602957853695
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0405 -0.7659 0.0196 0.8877 4.3310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97631 0.09199 108.450 < 2e-16 ***
## category_code_LT01_2_count 0.74834 0.08763 8.540 < 2e-16 ***
## category_code_LT01_5_count 0.98002 0.06213 15.774 < 2e-16 ***
## category_code_LT01_10_count 0.13927 0.11402 1.221 0.222
## category_code_LT01_11_count 0.51085 0.11688 4.371 1.51e-05 ***
## category_code_LT01_12_count -0.09209 0.21587 -0.427 0.670
## category_code_LT01_16_count 0.15559 1.18854 0.131 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6153, Adjusted R-squared: 0.6106
## F-statistic: 130.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611089433007747
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0398 -0.7709 0.0348 0.9079 4.3463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98136 0.09233 108.111 < 2e-16 ***
## category_code_LT01_2_count 0.73857 0.08758 8.433 3.79e-16 ***
## category_code_LT01_5_count 0.97170 0.06233 15.591 < 2e-16 ***
## category_code_LT01_10_count 0.12006 0.11661 1.030 0.304
## category_code_LT01_11_count 0.48937 0.11353 4.311 1.97e-05 ***
## category_code_LT01_13_count 0.15635 0.24651 0.634 0.526
## category_code_LT01_14_count 0.21713 0.33765 0.643 0.520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6158, Adjusted R-squared: 0.6111
## F-statistic: 131.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.610831534159635
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0381 -0.7658 0.0269 0.9012 4.3445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97625 0.09195 108.491 < 2e-16 ***
## category_code_LT01_2_count 0.74186 0.08749 8.479 2.68e-16 ***
## category_code_LT01_5_count 0.97662 0.06190 15.776 < 2e-16 ***
## category_code_LT01_10_count 0.13346 0.11429 1.168 0.243
## category_code_LT01_11_count 0.49024 0.11375 4.310 1.97e-05 ***
## category_code_LT01_13_count 0.16100 0.24710 0.652 0.515
## category_code_LT01_15_count 0.22651 0.76409 0.296 0.767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610783458735654
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0376 -0.7656 0.0305 0.8998 4.3437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97606 0.09197 108.475 < 2e-16 ***
## category_code_LT01_2_count 0.74236 0.08774 8.461 3.07e-16 ***
## category_code_LT01_5_count 0.97628 0.06192 15.767 < 2e-16 ***
## category_code_LT01_10_count 0.13551 0.11399 1.189 0.235
## category_code_LT01_11_count 0.49301 0.11347 4.345 1.69e-05 ***
## category_code_LT01_13_count 0.15800 0.24683 0.640 0.522
## category_code_LT01_16_count 0.19618 1.18917 0.165 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610822594783957
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0414 -0.7714 0.0242 0.9019 4.3413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98188 0.09238 108.058 < 2e-16 ***
## category_code_LT01_2_count 0.74072 0.08765 8.451 3.31e-16 ***
## category_code_LT01_5_count 0.97324 0.06231 15.619 < 2e-16 ***
## category_code_LT01_10_count 0.11987 0.11696 1.025 0.306
## category_code_LT01_11_count 0.49281 0.11351 4.342 1.72e-05 ***
## category_code_LT01_14_count 0.21713 0.33777 0.643 0.521
## category_code_LT01_15_count 0.19492 0.76251 0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610797025834611
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.7715 0.0324 0.9024 4.3415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98190 0.09240 108.029 < 2e-16 ***
## category_code_LT01_2_count 0.74061 0.08796 8.420 4.18e-16 ***
## category_code_LT01_5_count 0.97279 0.06233 15.606 < 2e-16 ***
## category_code_LT01_10_count 0.12113 0.11675 1.037 0.300
## category_code_LT01_11_count 0.49513 0.11327 4.371 1.51e-05 ***
## category_code_LT01_14_count 0.22120 0.33856 0.653 0.514
## category_code_LT01_16_count 0.21659 1.19081 0.182 0.856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.405 on 491 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6108
## F-statistic: 131 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610512485665526
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0392 -0.7661 0.0197 0.8963 4.3384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97655 0.09202 108.419 < 2e-16 ***
## category_code_LT01_2_count 0.74468 0.08781 8.481 2.65e-16 ***
## category_code_LT01_5_count 0.97785 0.06190 15.797 < 2e-16 ***
## category_code_LT01_10_count 0.13537 0.11435 1.184 0.237
## category_code_LT01_11_count 0.49643 0.11345 4.376 1.48e-05 ***
## category_code_LT01_15_count 0.19891 0.76342 0.261 0.795
## category_code_LT01_16_count 0.17627 1.18946 0.148 0.882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6105
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.61041932755523
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0688 -0.7940 0.0044 0.8905 4.2958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00900 0.08865 112.910 < 2e-16 ***
## category_code_LT01_2_count 0.74957 0.08727 8.589 < 2e-16 ***
## category_code_LT01_5_count 0.97367 0.06257 15.561 < 2e-16 ***
## category_code_LT01_11_count 0.50760 0.11715 4.333 1.78e-05 ***
## category_code_LT01_12_count -0.09999 0.21632 -0.462 0.644
## category_code_LT01_13_count 0.16509 0.24663 0.669 0.504
## category_code_LT01_14_count 0.30255 0.33077 0.915 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.609869864779599
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0702 -0.7692 -0.0032 0.8926 4.2857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00585 0.08865 112.875 < 2e-16 ***
## category_code_LT01_2_count 0.75591 0.08710 8.679 < 2e-16 ***
## category_code_LT01_5_count 0.98032 0.06220 15.761 < 2e-16 ***
## category_code_LT01_11_count 0.50789 0.11755 4.321 1.88e-05 ***
## category_code_LT01_12_count -0.08370 0.21609 -0.387 0.699
## category_code_LT01_13_count 0.17201 0.24727 0.696 0.487
## category_code_LT01_15_count 0.28943 0.76292 0.379 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6099
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609788639261177
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0703 -0.7689 -0.0028 0.8998 4.2833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00618 0.08866 112.857 < 2e-16 ***
## category_code_LT01_2_count 0.75691 0.08732 8.669 < 2e-16 ***
## category_code_LT01_5_count 0.97998 0.06222 15.750 < 2e-16 ***
## category_code_LT01_11_count 0.51184 0.11717 4.368 1.53e-05 ***
## category_code_LT01_12_count -0.08580 0.21601 -0.397 0.691
## category_code_LT01_13_count 0.16831 0.24704 0.681 0.496
## category_code_LT01_16_count 0.24295 1.18996 0.204 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610145318392828
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0703 -0.7774 -0.0086 0.8861 4.2915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00946 0.08867 112.879 < 2e-16 ***
## category_code_LT01_2_count 0.75140 0.08739 8.598 < 2e-16 ***
## category_code_LT01_5_count 0.97523 0.06256 15.589 < 2e-16 ***
## category_code_LT01_11_count 0.51026 0.11725 4.352 1.64e-05 ***
## category_code_LT01_12_count -0.09578 0.21652 -0.442 0.658
## category_code_LT01_14_count 0.30171 0.33094 0.912 0.362
## category_code_LT01_15_count 0.24391 0.76133 0.320 0.749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6149, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610105580946325
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0703 -0.7773 0.0024 0.8884 4.2911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00986 0.08869 112.865 < 2e-16 ***
## category_code_LT01_2_count 0.75140 0.08770 8.568 < 2e-16 ***
## category_code_LT01_5_count 0.97470 0.06258 15.574 < 2e-16 ***
## category_code_LT01_11_count 0.51349 0.11691 4.392 1.37e-05 ***
## category_code_LT01_12_count -0.09768 0.21641 -0.451 0.652
## category_code_LT01_14_count 0.30803 0.33144 0.929 0.353
## category_code_LT01_16_count 0.27297 1.19042 0.229 0.819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.6101
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609513783357912
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0719 -0.7690 -0.0147 0.8909 4.2790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00663 0.08869 112.826 < 2e-16 ***
## category_code_LT01_2_count 0.75875 0.08745 8.676 < 2e-16 ***
## category_code_LT01_5_count 0.98157 0.06220 15.780 < 2e-16 ***
## category_code_LT01_11_count 0.51434 0.11727 4.386 1.41e-05 ***
## category_code_LT01_12_count -0.08147 0.21621 -0.377 0.706
## category_code_LT01_15_count 0.26216 0.76235 0.344 0.731
## category_code_LT01_16_count 0.22494 1.19020 0.189 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6095
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.610363331818727
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0665 -0.7756 0.0045 0.8904 4.3062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00816 0.08864 112.911 < 2e-16 ***
## category_code_LT01_2_count 0.74494 0.08754 8.510 < 2e-16 ***
## category_code_LT01_5_count 0.97158 0.06239 15.574 < 2e-16 ***
## category_code_LT01_11_count 0.49099 0.11386 4.312 1.95e-05 ***
## category_code_LT01_13_count 0.16951 0.24711 0.686 0.493
## category_code_LT01_14_count 0.29002 0.33006 0.879 0.380
## category_code_LT01_15_count 0.28827 0.76211 0.378 0.705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610304243414326
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0665 -0.7760 0.0160 0.8961 4.3055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00861 0.08865 112.894 < 2e-16 ***
## category_code_LT01_2_count 0.74508 0.08785 8.481 2.63e-16 ***
## category_code_LT01_5_count 0.97095 0.06241 15.557 < 2e-16 ***
## category_code_LT01_11_count 0.49460 0.11355 4.356 1.62e-05 ***
## category_code_LT01_13_count 0.16636 0.24686 0.674 0.501
## category_code_LT01_14_count 0.29710 0.33060 0.899 0.369
## category_code_LT01_16_count 0.31197 1.19104 0.262 0.793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.615, Adjusted R-squared: 0.6103
## F-statistic: 130.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609791156768943
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0683 -0.7799 0.0018 0.8912 4.2946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00557 0.08865 112.864 < 2e-16 ***
## category_code_LT01_2_count 0.75177 0.08759 8.583 < 2e-16 ***
## category_code_LT01_5_count 0.97794 0.06198 15.777 < 2e-16 ***
## category_code_LT01_11_count 0.49674 0.11378 4.366 1.55e-05 ***
## category_code_LT01_13_count 0.17346 0.24752 0.701 0.484
## category_code_LT01_15_count 0.30615 0.76316 0.401 0.688
## category_code_LT01_16_count 0.26880 1.19075 0.226 0.821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_2_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610038253404727
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0681 -0.7596 0.0021 0.8892 4.3011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00908 0.08868 112.865 < 2e-16 ***
## category_code_LT01_2_count 0.74698 0.08795 8.493 2.41e-16 ***
## category_code_LT01_5_count 0.97266 0.06239 15.589 < 2e-16 ***
## category_code_LT01_11_count 0.49764 0.11355 4.383 1.43e-05 ***
## category_code_LT01_14_count 0.29629 0.33074 0.896 0.371
## category_code_LT01_15_count 0.26261 0.76145 0.345 0.730
## category_code_LT01_16_count 0.29384 1.19122 0.247 0.805
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.61
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count 0.6313171063544
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9857 -0.7511 0.0468 0.9200 3.4749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97623 0.08631 115.590 < 2e-16 ***
## category_code_LT01_3_count 0.32970 0.11172 2.951 0.00332 **
## category_code_LT01_4_count 0.74209 0.09008 8.238 1.6e-15 ***
## category_code_LT01_5_count 0.90104 0.06219 14.488 < 2e-16 ***
## category_code_LT01_6_count 0.45189 0.14897 3.033 0.00255 **
## category_code_LT01_7_count 0.49602 0.15181 3.267 0.00116 **
## category_code_LT01_8_count -0.20677 0.27102 -0.763 0.44589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6313
## F-statistic: 142.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count 0.632432615550202
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9723 -0.7485 0.0414 0.9309 3.5253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97116 0.08617 115.711 < 2e-16 ***
## category_code_LT01_3_count 0.30442 0.11277 2.699 0.00719 **
## category_code_LT01_4_count 0.73584 0.09008 8.169 2.65e-15 ***
## category_code_LT01_5_count 0.88904 0.06157 14.439 < 2e-16 ***
## category_code_LT01_6_count 0.43610 0.14887 2.929 0.00355 **
## category_code_LT01_7_count 0.47007 0.15231 3.086 0.00214 **
## category_code_LT01_9_count 0.32375 0.22481 1.440 0.15048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.365 on 491 degrees of freedom
## Multiple R-squared: 0.6369, Adjusted R-squared: 0.6324
## F-statistic: 143.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count 0.630934310987496
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9731 -0.7524 0.0166 0.9352 3.4608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96815 0.08941 111.482 < 2e-16 ***
## category_code_LT01_3_count 0.32332 0.11334 2.853 0.00452 **
## category_code_LT01_4_count 0.74346 0.09014 8.247 1.49e-15 ***
## category_code_LT01_5_count 0.89455 0.06160 14.523 < 2e-16 ***
## category_code_LT01_6_count 0.44164 0.15067 2.931 0.00353 **
## category_code_LT01_7_count 0.48932 0.15228 3.213 0.00140 **
## category_code_LT01_10_count 0.03062 0.11398 0.269 0.78834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count 0.634907531644201
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9846 -0.7495 0.0248 0.9038 3.4724
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98023 0.08589 116.199 < 2e-16 ***
## category_code_LT01_3_count 0.26391 0.11456 2.304 0.02166 *
## category_code_LT01_4_count 0.65876 0.09666 6.815 2.77e-11 ***
## category_code_LT01_5_count 0.89380 0.06126 14.591 < 2e-16 ***
## category_code_LT01_6_count 0.38869 0.15030 2.586 0.00999 **
## category_code_LT01_7_count 0.40001 0.15614 2.562 0.01071 *
## category_code_LT01_11_count 0.27250 0.11709 2.327 0.02035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.361 on 491 degrees of freedom
## Multiple R-squared: 0.6393, Adjusted R-squared: 0.6349
## F-statistic: 145 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count 0.63095726944896
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9779 -0.7390 0.0211 0.9280 3.4838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97423 0.08632 115.552 < 2e-16 ***
## category_code_LT01_3_count 0.32586 0.11204 2.908 0.00380 **
## category_code_LT01_4_count 0.73973 0.09068 8.158 2.88e-15 ***
## category_code_LT01_5_count 0.89270 0.06178 14.449 < 2e-16 ***
## category_code_LT01_6_count 0.44192 0.15005 2.945 0.00338 **
## category_code_LT01_7_count 0.49190 0.15183 3.240 0.00128 **
## category_code_LT01_12_count 0.06540 0.20408 0.320 0.74875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.631
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count 0.630901955751937
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9789 -0.7352 0.0136 0.9324 3.4797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97433 0.08632 115.545 < 2e-16 ***
## category_code_LT01_3_count 0.32809 0.11179 2.935 0.00349 **
## category_code_LT01_4_count 0.74154 0.09050 8.193 2.22e-15 ***
## category_code_LT01_5_count 0.89407 0.06160 14.513 < 2e-16 ***
## category_code_LT01_6_count 0.44807 0.14897 3.008 0.00277 **
## category_code_LT01_7_count 0.48939 0.15291 3.200 0.00146 **
## category_code_LT01_13_count 0.04129 0.24203 0.171 0.86461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count 0.631157082663155
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9780 -0.7377 0.0393 0.9234 3.4805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97617 0.08634 115.544 < 2e-16 ***
## category_code_LT01_3_count 0.33066 0.11180 2.958 0.00325 **
## category_code_LT01_4_count 0.73162 0.09201 7.952 1.27e-14 ***
## category_code_LT01_5_count 0.89009 0.06195 14.367 < 2e-16 ***
## category_code_LT01_6_count 0.45536 0.14943 3.047 0.00243 **
## category_code_LT01_7_count 0.48564 0.15219 3.191 0.00151 **
## category_code_LT01_14_count 0.19770 0.32556 0.607 0.54397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6356, Adjusted R-squared: 0.6312
## F-statistic: 142.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count 0.630880610519418
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9791 -0.7354 0.0114 0.9397 3.4784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97440 0.08633 115.539 < 2e-16 ***
## category_code_LT01_3_count 0.32876 0.11270 2.917 0.00369 **
## category_code_LT01_4_count 0.74310 0.09032 8.227 1.73e-15 ***
## category_code_LT01_5_count 0.89423 0.06162 14.513 < 2e-16 ***
## category_code_LT01_6_count 0.44790 0.14908 3.004 0.00280 **
## category_code_LT01_7_count 0.49235 0.15192 3.241 0.00127 **
## category_code_LT01_15_count -0.01990 0.74700 -0.027 0.97875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6353, Adjusted R-squared: 0.6309
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_16_count 0.631509257578926
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9783 -0.7394 0.0196 0.9407 3.5037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97461 0.08625 115.645 < 2e-16 ***
## category_code_LT01_3_count 0.31456 0.11270 2.791 0.00546 **
## category_code_LT01_4_count 0.74212 0.09006 8.240 1.57e-15 ***
## category_code_LT01_5_count 0.89271 0.06156 14.501 < 2e-16 ***
## category_code_LT01_6_count 0.45825 0.14928 3.070 0.00226 **
## category_code_LT01_7_count 0.49282 0.15170 3.249 0.00124 **
## category_code_LT01_16_count 1.06222 1.16011 0.916 0.36032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.636, Adjusted R-squared: 0.6315
## F-statistic: 143 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count 0.625685477923506
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9843 -0.7620 0.0403 0.9049 3.4986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97242 0.08699 114.636 < 2e-16 ***
## category_code_LT01_3_count 0.31861 0.11373 2.801 0.00529 **
## category_code_LT01_4_count 0.82632 0.08592 9.618 < 2e-16 ***
## category_code_LT01_5_count 0.90450 0.06270 14.426 < 2e-16 ***
## category_code_LT01_6_count 0.44336 0.15032 2.950 0.00333 **
## category_code_LT01_8_count -0.19366 0.27307 -0.709 0.47854
## category_code_LT01_9_count 0.39927 0.22577 1.768 0.07760 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count 0.623511273229027
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9808 -0.7536 -0.0113 0.9104 3.4044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96405 0.09032 110.323 < 2e-16 ***
## category_code_LT01_3_count 0.33900 0.11439 2.963 0.00319 **
## category_code_LT01_4_count 0.84037 0.08579 9.796 < 2e-16 ***
## category_code_LT01_5_count 0.91158 0.06278 14.521 < 2e-16 ***
## category_code_LT01_6_count 0.44579 0.15227 2.928 0.00357 **
## category_code_LT01_8_count -0.18174 0.27378 -0.664 0.50712
## category_code_LT01_10_count 0.06008 0.11479 0.523 0.60094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6235
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count 0.630264975463631
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9970 -0.7526 0.0552 0.8988 3.4547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98341 0.08646 115.467 < 2e-16 ***
## category_code_LT01_3_count 0.26228 0.11532 2.274 0.02337 *
## category_code_LT01_4_count 0.71013 0.09522 7.458 4.02e-13 ***
## category_code_LT01_5_count 0.90700 0.06222 14.578 < 2e-16 ***
## category_code_LT01_6_count 0.38058 0.15132 2.515 0.01222 *
## category_code_LT01_8_count -0.15243 0.27143 -0.562 0.57466
## category_code_LT01_11_count 0.34671 0.11401 3.041 0.00248 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6347, Adjusted R-squared: 0.6303
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count 0.62340972209746
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9913 -0.7630 0.0117 0.9081 3.4594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97613 0.08723 114.367 < 2e-16 ***
## category_code_LT01_3_count 0.34616 0.11303 3.062 0.00232 **
## category_code_LT01_4_count 0.83663 0.08644 9.679 < 2e-16 ***
## category_code_LT01_5_count 0.90933 0.06296 14.444 < 2e-16 ***
## category_code_LT01_6_count 0.45096 0.15164 2.974 0.00309 **
## category_code_LT01_8_count -0.18292 0.27392 -0.668 0.50459
## category_code_LT01_12_count 0.07757 0.20624 0.376 0.70700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count 0.623502260025409
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9916 -0.7632 0.0181 0.9001 3.4588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97600 0.08722 114.378 < 2e-16 ***
## category_code_LT01_3_count 0.34785 0.11277 3.085 0.00215 **
## category_code_LT01_4_count 0.83449 0.08661 9.636 < 2e-16 ***
## category_code_LT01_5_count 0.91000 0.06281 14.489 < 2e-16 ***
## category_code_LT01_6_count 0.45852 0.15054 3.046 0.00244 **
## category_code_LT01_8_count -0.17162 0.27421 -0.626 0.53170
## category_code_LT01_13_count 0.12449 0.24312 0.512 0.60886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.628, Adjusted R-squared: 0.6235
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count 0.623855025976649
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9911 -0.7471 0.0186 0.9074 3.4613
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97883 0.08723 114.403 < 2e-16 ***
## category_code_LT01_3_count 0.35202 0.11274 3.122 0.00190 **
## category_code_LT01_4_count 0.82268 0.08829 9.318 < 2e-16 ***
## category_code_LT01_5_count 0.90514 0.06313 14.337 < 2e-16 ***
## category_code_LT01_6_count 0.46851 0.15099 3.103 0.00203 **
## category_code_LT01_8_count -0.18407 0.27367 -0.673 0.50152
## category_code_LT01_14_count 0.27881 0.32792 0.850 0.39561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6284, Adjusted R-squared: 0.6239
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count 0.623313800737415
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9925 -0.7532 0.0151 0.9121 3.4576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97622 0.08724 114.350 < 2e-16 ***
## category_code_LT01_3_count 0.35099 0.11367 3.088 0.00213 **
## category_code_LT01_4_count 0.84121 0.08595 9.787 < 2e-16 ***
## category_code_LT01_5_count 0.91087 0.06281 14.501 < 2e-16 ***
## category_code_LT01_6_count 0.45856 0.15069 3.043 0.00247 **
## category_code_LT01_8_count -0.17947 0.27383 -0.655 0.51251
## category_code_LT01_15_count -0.09656 0.75422 -0.128 0.89818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6233
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_16_count 0.623968169859069
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9922 -0.7528 0.0065 0.9050 3.4650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97664 0.08716 114.459 < 2e-16 ***
## category_code_LT01_3_count 0.33501 0.11369 2.947 0.00336 **
## category_code_LT01_4_count 0.83979 0.08574 9.795 < 2e-16 ***
## category_code_LT01_5_count 0.90994 0.06274 14.502 < 2e-16 ***
## category_code_LT01_6_count 0.46890 0.15091 3.107 0.00200 **
## category_code_LT01_8_count -0.19272 0.27394 -0.704 0.48207
## category_code_LT01_16_count 1.09504 1.17343 0.933 0.35118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count 0.625394445419011
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9704 -0.7628 0.0272 0.9266 3.4773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96265 0.09007 110.614 < 2e-16 ***
## category_code_LT01_3_count 0.31141 0.11515 2.704 0.00708 **
## category_code_LT01_4_count 0.82675 0.08595 9.619 < 2e-16 ***
## category_code_LT01_5_count 0.89858 0.06210 14.471 < 2e-16 ***
## category_code_LT01_6_count 0.43186 0.15192 2.843 0.00466 **
## category_code_LT01_9_count 0.38701 0.22683 1.706 0.08860 .
## category_code_LT01_10_count 0.04003 0.11503 0.348 0.72799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count 0.631937045307109
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9840 -0.7678 0.0607 0.9137 3.4722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97829 0.08627 115.666 < 2e-16 ***
## category_code_LT01_3_count 0.23573 0.11608 2.031 0.04281 *
## category_code_LT01_4_count 0.70046 0.09516 7.361 7.77e-13 ***
## category_code_LT01_5_count 0.89569 0.06155 14.553 < 2e-16 ***
## category_code_LT01_6_count 0.36606 0.15100 2.424 0.01570 *
## category_code_LT01_9_count 0.35772 0.22413 1.596 0.11112
## category_code_LT01_11_count 0.33876 0.11387 2.975 0.00307 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 491 degrees of freedom
## Multiple R-squared: 0.6364, Adjusted R-squared: 0.6319
## F-statistic: 143.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count 0.625395048855176
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9768 -0.7601 0.0330 0.9230 3.5072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97056 0.08700 114.609 < 2e-16 ***
## category_code_LT01_3_count 0.31483 0.11404 2.761 0.00598 **
## category_code_LT01_4_count 0.82299 0.08658 9.506 < 2e-16 ***
## category_code_LT01_5_count 0.89643 0.06229 14.392 < 2e-16 ***
## category_code_LT01_6_count 0.43323 0.15139 2.862 0.00439 **
## category_code_LT01_9_count 0.39431 0.22577 1.747 0.08134 .
## category_code_LT01_12_count 0.07178 0.20560 0.349 0.72713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count 0.625631552184243
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9769 -0.7599 0.0366 0.9171 3.5066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97034 0.08697 114.642 < 2e-16 ***
## category_code_LT01_3_count 0.31531 0.11378 2.771 0.00580 **
## category_code_LT01_4_count 0.81850 0.08680 9.429 < 2e-16 ***
## category_code_LT01_5_count 0.89694 0.06209 14.445 < 2e-16 ***
## category_code_LT01_6_count 0.44041 0.15024 2.931 0.00353 **
## category_code_LT01_9_count 0.40362 0.22610 1.785 0.07486 .
## category_code_LT01_13_count 0.15940 0.24247 0.657 0.51124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count 0.62567561870785
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9770 -0.7625 0.0321 0.9185 3.4939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97292 0.08702 114.610 < 2e-16 ***
## category_code_LT01_3_count 0.32092 0.11382 2.819 0.00500 **
## category_code_LT01_4_count 0.81234 0.08831 9.199 < 2e-16 ***
## category_code_LT01_5_count 0.89336 0.06244 14.307 < 2e-16 ***
## category_code_LT01_6_count 0.44883 0.15080 2.976 0.00306 **
## category_code_LT01_9_count 0.38176 0.22643 1.686 0.09243 .
## category_code_LT01_14_count 0.22971 0.32816 0.700 0.48425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count 0.625305249431648
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9781 -0.7580 0.0285 0.9211 3.5004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97072 0.08701 114.595 < 2e-16 ***
## category_code_LT01_3_count 0.31857 0.11476 2.776 0.00572 **
## category_code_LT01_4_count 0.82696 0.08612 9.603 < 2e-16 ***
## category_code_LT01_5_count 0.89807 0.06211 14.458 < 2e-16 ***
## category_code_LT01_6_count 0.44001 0.15043 2.925 0.00360 **
## category_code_LT01_9_count 0.39407 0.22596 1.744 0.08179 .
## category_code_LT01_15_count -0.04876 0.75278 -0.065 0.94838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_16_count 0.625846577862425
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9775 -0.7574 0.0286 0.9304 3.5240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97101 0.08694 114.685 < 2e-16 ***
## category_code_LT01_3_count 0.30521 0.11464 2.662 0.00801 **
## category_code_LT01_4_count 0.82614 0.08590 9.618 < 2e-16 ***
## category_code_LT01_5_count 0.89683 0.06207 14.449 < 2e-16 ***
## category_code_LT01_6_count 0.44962 0.15066 2.984 0.00298 **
## category_code_LT01_9_count 0.38854 0.22574 1.721 0.08585 .
## category_code_LT01_16_count 0.98868 1.16958 0.845 0.39834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6258
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count 0.630236723397719
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9802 -0.7457 0.0225 0.9162 3.4075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96985 0.08951 111.378 < 2e-16 ***
## category_code_LT01_3_count 0.25049 0.11689 2.143 0.03261 *
## category_code_LT01_4_count 0.70917 0.09522 7.448 4.3e-13 ***
## category_code_LT01_5_count 0.90233 0.06157 14.656 < 2e-16 ***
## category_code_LT01_6_count 0.36497 0.15291 2.387 0.01737 *
## category_code_LT01_10_count 0.05996 0.11375 0.527 0.59837
## category_code_LT01_11_count 0.34899 0.11395 3.063 0.00232 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6347, Adjusted R-squared: 0.6302
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count 0.623265230706741
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9740 -0.7522 -0.0034 0.9263 3.4150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96262 0.09032 110.300 < 2e-16 ***
## category_code_LT01_3_count 0.33525 0.11467 2.924 0.00362 **
## category_code_LT01_4_count 0.83687 0.08645 9.680 < 2e-16 ***
## category_code_LT01_5_count 0.90382 0.06234 14.498 < 2e-16 ***
## category_code_LT01_6_count 0.43612 0.15328 2.845 0.00462 **
## category_code_LT01_10_count 0.05827 0.11483 0.507 0.61211
## category_code_LT01_12_count 0.07134 0.20621 0.346 0.72951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6233
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count 0.623391792407164
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9747 -0.7523 -0.0055 0.9141 3.4151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96278 0.09031 110.321 < 2e-16 ***
## category_code_LT01_3_count 0.33692 0.11441 2.945 0.00339 **
## category_code_LT01_4_count 0.83416 0.08662 9.630 < 2e-16 ***
## category_code_LT01_5_count 0.90467 0.06215 14.556 < 2e-16 ***
## category_code_LT01_6_count 0.44361 0.15222 2.914 0.00373 **
## category_code_LT01_10_count 0.05714 0.11485 0.498 0.61902
## category_code_LT01_13_count 0.12959 0.24286 0.534 0.59385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6279, Adjusted R-squared: 0.6234
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count 0.623587355064072
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9779 -0.7435 -0.0018 0.9169 3.4350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96918 0.09072 109.894 < 2e-16 ***
## category_code_LT01_3_count 0.34399 0.11467 3.000 0.00284 **
## category_code_LT01_4_count 0.82463 0.08845 9.323 < 2e-16 ***
## category_code_LT01_5_count 0.89996 0.06258 14.382 < 2e-16 ***
## category_code_LT01_6_count 0.45601 0.15330 2.975 0.00308 **
## category_code_LT01_10_count 0.03795 0.11829 0.321 0.74849
## category_code_LT01_14_count 0.24841 0.33805 0.735 0.46279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6236
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count 0.623193418146389
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9747 -0.7519 -0.0118 0.9284 3.4117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96232 0.09036 110.255 < 2e-16 ***
## category_code_LT01_3_count 0.34005 0.11520 2.952 0.00331 **
## category_code_LT01_4_count 0.84129 0.08596 9.787 < 2e-16 ***
## category_code_LT01_5_count 0.90527 0.06217 14.562 < 2e-16 ***
## category_code_LT01_6_count 0.44308 0.15231 2.909 0.00379 **
## category_code_LT01_10_count 0.06004 0.11500 0.522 0.60186
## category_code_LT01_15_count -0.12205 0.75549 -0.162 0.87173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6277, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_16_count 0.623764622344067
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9752 -0.7513 -0.0143 0.9234 3.4352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96369 0.09027 110.376 < 2e-16 ***
## category_code_LT01_3_count 0.32519 0.11525 2.821 0.00497 **
## category_code_LT01_4_count 0.83978 0.08576 9.792 < 2e-16 ***
## category_code_LT01_5_count 0.90402 0.06212 14.553 < 2e-16 ***
## category_code_LT01_6_count 0.45334 0.15264 2.970 0.00312 **
## category_code_LT01_10_count 0.05495 0.11483 0.479 0.63247
## category_code_LT01_16_count 1.03051 1.17318 0.878 0.38016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6238
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count 0.630151935948373
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9940 -0.7513 0.0352 0.9070 3.4589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98261 0.08645 115.469 < 2e-16 ***
## category_code_LT01_3_count 0.26106 0.11530 2.264 0.02400 *
## category_code_LT01_4_count 0.70937 0.09523 7.449 4.26e-13 ***
## category_code_LT01_5_count 0.90391 0.06176 14.635 < 2e-16 ***
## category_code_LT01_6_count 0.38205 0.15171 2.518 0.01211 *
## category_code_LT01_11_count 0.36058 0.11758 3.067 0.00228 **
## category_code_LT01_12_count -0.08566 0.21076 -0.406 0.68459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6346, Adjusted R-squared: 0.6302
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count 0.630132702331056
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9915 -0.7417 0.0337 0.9002 3.4624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98187 0.08645 115.466 < 2e-16 ***
## category_code_LT01_3_count 0.26040 0.11530 2.258 0.02436 *
## category_code_LT01_4_count 0.70598 0.09568 7.379 6.88e-13 ***
## category_code_LT01_5_count 0.90134 0.06159 14.634 < 2e-16 ***
## category_code_LT01_6_count 0.37822 0.15125 2.501 0.01272 *
## category_code_LT01_11_count 0.34630 0.11417 3.033 0.00255 **
## category_code_LT01_13_count 0.09006 0.24099 0.374 0.70878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6346, Adjusted R-squared: 0.6301
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count 0.630377062738799
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9907 -0.7403 0.0408 0.8896 3.4650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98398 0.08646 115.474 < 2e-16 ***
## category_code_LT01_3_count 0.26397 0.11537 2.288 0.02255 *
## category_code_LT01_4_count 0.69678 0.09699 7.184 2.53e-12 ***
## category_code_LT01_5_count 0.89710 0.06196 14.479 < 2e-16 ***
## category_code_LT01_6_count 0.38640 0.15179 2.546 0.01121 *
## category_code_LT01_11_count 0.34464 0.11410 3.021 0.00265 **
## category_code_LT01_14_count 0.22180 0.32548 0.681 0.49592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6348, Adjusted R-squared: 0.6304
## F-statistic: 142.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count 0.630062819287676
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9917 -0.7455 0.0366 0.9135 3.4622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98195 0.08646 115.455 < 2e-16 ***
## category_code_LT01_3_count 0.26358 0.11610 2.270 0.0236 *
## category_code_LT01_4_count 0.71024 0.09531 7.452 4.19e-13 ***
## category_code_LT01_5_count 0.90154 0.06161 14.634 < 2e-16 ***
## category_code_LT01_6_count 0.37816 0.15132 2.499 0.0128 *
## category_code_LT01_11_count 0.34949 0.11402 3.065 0.0023 **
## category_code_LT01_15_count -0.16189 0.74769 -0.217 0.8287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.6301
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_16_count 0.630559138090662
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9913 -0.7482 0.0336 0.9168 3.4630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98225 0.08640 115.540 < 2e-16 ***
## category_code_LT01_3_count 0.24849 0.11614 2.140 0.03289 *
## category_code_LT01_4_count 0.70953 0.09518 7.455 4.1e-13 ***
## category_code_LT01_5_count 0.90052 0.06156 14.628 < 2e-16 ***
## category_code_LT01_6_count 0.38715 0.15161 2.554 0.01096 *
## category_code_LT01_11_count 0.34676 0.11393 3.044 0.00246 **
## category_code_LT01_16_count 0.97664 1.16187 0.841 0.40099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.635, Adjusted R-squared: 0.6306
## F-statistic: 142.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count 0.623290299970919
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9848 -0.7510 0.0120 0.9185 3.4687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97427 0.08721 114.369 < 2e-16 ***
## category_code_LT01_3_count 0.34385 0.11306 3.041 0.00248 **
## category_code_LT01_4_count 0.83074 0.08722 9.525 < 2e-16 ***
## category_code_LT01_5_count 0.90257 0.06235 14.476 < 2e-16 ***
## category_code_LT01_6_count 0.44885 0.15160 2.961 0.00322 **
## category_code_LT01_12_count 0.07001 0.20626 0.339 0.73443
## category_code_LT01_13_count 0.13083 0.24289 0.539 0.59038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6233
## F-statistic: 138.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count 0.623568946370718
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9842 -0.7480 0.0018 0.9278 3.4712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97693 0.08723 114.377 < 2e-16 ***
## category_code_LT01_3_count 0.34833 0.11307 3.081 0.00218 **
## category_code_LT01_4_count 0.82066 0.08874 9.248 < 2e-16 ***
## category_code_LT01_5_count 0.89790 0.06268 14.326 < 2e-16 ***
## category_code_LT01_6_count 0.45912 0.15220 3.017 0.00269 **
## category_code_LT01_12_count 0.05813 0.20695 0.281 0.77891
## category_code_LT01_14_count 0.26630 0.32935 0.809 0.41915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6236
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count 0.623079193509732
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9853 -0.7526 0.0095 0.9315 3.4679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97444 0.08724 114.335 < 2e-16 ***
## category_code_LT01_3_count 0.34682 0.11401 3.042 0.00248 **
## category_code_LT01_4_count 0.83761 0.08663 9.669 < 2e-16 ***
## category_code_LT01_5_count 0.90318 0.06237 14.481 < 2e-16 ***
## category_code_LT01_6_count 0.44842 0.15177 2.955 0.00328 **
## category_code_LT01_12_count 0.07256 0.20632 0.352 0.72523
## category_code_LT01_15_count -0.09233 0.75476 -0.122 0.90268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6276, Adjusted R-squared: 0.6231
## F-statistic: 137.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_16_count 0.623686052669408
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9847 -0.7615 0.0026 0.9228 3.4735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97473 0.08716 114.435 < 2e-16 ***
## category_code_LT01_3_count 0.33134 0.11400 2.906 0.00382 **
## category_code_LT01_4_count 0.83623 0.08641 9.678 < 2e-16 ***
## category_code_LT01_5_count 0.90184 0.06232 14.470 < 2e-16 ***
## category_code_LT01_6_count 0.45805 0.15195 3.014 0.00271 **
## category_code_LT01_12_count 0.07328 0.20606 0.356 0.72229
## category_code_LT01_16_count 1.05303 1.17235 0.898 0.36951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6282, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count 0.623740581096413
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9844 -0.7483 0.0018 0.8978 3.4708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97690 0.08721 114.407 < 2e-16 ***
## category_code_LT01_3_count 0.34934 0.11276 3.098 0.00206 **
## category_code_LT01_4_count 0.81653 0.08910 9.164 < 2e-16 ***
## category_code_LT01_5_count 0.89822 0.06253 14.364 < 2e-16 ***
## category_code_LT01_6_count 0.46558 0.15091 3.085 0.00215 **
## category_code_LT01_13_count 0.13354 0.24264 0.550 0.58231
## category_code_LT01_14_count 0.27493 0.32792 0.838 0.40220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6283, Adjusted R-squared: 0.6237
## F-statistic: 138.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count 0.623209186742556
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9859 -0.7544 0.0105 0.9187 3.4669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97439 0.08722 114.355 < 2e-16 ***
## category_code_LT01_3_count 0.34796 0.11373 3.060 0.00234 **
## category_code_LT01_4_count 0.83474 0.08682 9.614 < 2e-16 ***
## category_code_LT01_5_count 0.90409 0.06218 14.539 < 2e-16 ***
## category_code_LT01_6_count 0.45567 0.15061 3.025 0.00261 **
## category_code_LT01_13_count 0.13173 0.24332 0.541 0.58848
## category_code_LT01_15_count -0.07364 0.75589 -0.097 0.92244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6278, Adjusted R-squared: 0.6232
## F-statistic: 138 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_16_count 0.623849480124218
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9852 -0.7514 0.0024 0.9186 3.4716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97465 0.08715 114.460 < 2e-16 ***
## category_code_LT01_3_count 0.33245 0.11374 2.923 0.00363 **
## category_code_LT01_4_count 0.83301 0.08657 9.622 < 2e-16 ***
## category_code_LT01_5_count 0.90262 0.06213 14.528 < 2e-16 ***
## category_code_LT01_6_count 0.46584 0.15082 3.089 0.00212 **
## category_code_LT01_13_count 0.14152 0.24277 0.583 0.56019
## category_code_LT01_16_count 1.07837 1.17290 0.919 0.35834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6284, Adjusted R-squared: 0.6238
## F-statistic: 138.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count 0.623525666505831
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9850 -0.7417 -0.0020 0.9296 3.4700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97706 0.08723 114.373 < 2e-16 ***
## category_code_LT01_3_count 0.35283 0.11369 3.104 0.00202 **
## category_code_LT01_4_count 0.82375 0.08844 9.314 < 2e-16 ***
## category_code_LT01_5_count 0.89884 0.06256 14.368 < 2e-16 ***
## category_code_LT01_6_count 0.46555 0.15107 3.082 0.00217 **
## category_code_LT01_14_count 0.27569 0.32808 0.840 0.40114
## category_code_LT01_15_count -0.11299 0.75415 -0.150 0.88097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6235
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_16_count 0.624187690150887
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9843 -0.7477 0.0008 0.9287 3.4714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97751 0.08715 114.483 < 2e-16 ***
## category_code_LT01_3_count 0.33647 0.11368 2.960 0.00323 **
## category_code_LT01_4_count 0.82130 0.08827 9.304 < 2e-16 ***
## category_code_LT01_5_count 0.89717 0.06251 14.353 < 2e-16 ***
## category_code_LT01_6_count 0.47615 0.15131 3.147 0.00175 **
## category_code_LT01_14_count 0.29017 0.32813 0.884 0.37696
## category_code_LT01_16_count 1.10504 1.17304 0.942 0.34664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_15_count+category_code_LT01_16_count 0.623596178090807
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9860 -0.7479 0.0019 0.9329 3.4671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97486 0.08718 114.420 < 2e-16 ***
## category_code_LT01_3_count 0.33563 0.11472 2.926 0.00360 **
## category_code_LT01_4_count 0.84043 0.08592 9.781 < 2e-16 ***
## category_code_LT01_5_count 0.90345 0.06215 14.536 < 2e-16 ***
## category_code_LT01_6_count 0.46511 0.15096 3.081 0.00218 **
## category_code_LT01_15_count -0.07232 0.75456 -0.096 0.92368
## category_code_LT01_16_count 1.04844 1.17349 0.893 0.37206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.382 on 491 degrees of freedom
## Multiple R-squared: 0.6281, Adjusted R-squared: 0.6236
## F-statistic: 138.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.626373383017677
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0078 -0.7609 0.0578 0.8577 3.4411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98951 0.08673 115.180 < 2e-16 ***
## category_code_LT01_3_count 0.34143 0.11304 3.020 0.00266 **
## category_code_LT01_4_count 0.81824 0.08625 9.486 < 2e-16 ***
## category_code_LT01_5_count 0.91381 0.06241 14.642 < 2e-16 ***
## category_code_LT01_7_count 0.47646 0.15362 3.102 0.00204 **
## category_code_LT01_8_count -0.18893 0.27276 -0.693 0.48884
## category_code_LT01_9_count 0.36387 0.22641 1.607 0.10867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.624808083358904
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9987 -0.7554 0.0522 0.8640 3.3574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97613 0.09015 110.658 < 2e-16 ***
## category_code_LT01_3_count 0.35443 0.11383 3.114 0.00196 **
## category_code_LT01_4_count 0.82700 0.08622 9.592 < 2e-16 ***
## category_code_LT01_5_count 0.92000 0.06243 14.737 < 2e-16 ***
## category_code_LT01_7_count 0.49293 0.15361 3.209 0.00142 **
## category_code_LT01_8_count -0.18012 0.27326 -0.659 0.51011
## category_code_LT01_10_count 0.08223 0.11363 0.724 0.46963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.630165200636706
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0168 -0.7735 0.0527 0.8651 3.4315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99745 0.08626 115.894 < 2e-16 ***
## category_code_LT01_3_count 0.28703 0.11505 2.495 0.01293 *
## category_code_LT01_4_count 0.71612 0.09477 7.556 2.04e-13 ***
## category_code_LT01_5_count 0.91535 0.06200 14.764 < 2e-16 ***
## category_code_LT01_7_count 0.39116 0.15720 2.488 0.01317 *
## category_code_LT01_8_count -0.15014 0.27144 -0.553 0.58043
## category_code_LT01_11_count 0.32133 0.11623 2.765 0.00591 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6346, Adjusted R-squared: 0.6302
## F-statistic: 142.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.624781187203298
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0126 -0.7658 0.0444 0.8656 3.4354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99272 0.08689 115.009 < 2e-16 ***
## category_code_LT01_3_count 0.36285 0.11233 3.230 0.00132 **
## category_code_LT01_4_count 0.81929 0.08724 9.391 < 2e-16 ***
## category_code_LT01_5_count 0.91608 0.06266 14.619 < 2e-16 ***
## category_code_LT01_7_count 0.50029 0.15315 3.267 0.00116 **
## category_code_LT01_8_count -0.18339 0.27340 -0.671 0.50267
## category_code_LT01_12_count 0.14284 0.20439 0.699 0.48496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.624414108032388
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0159 -0.7625 0.0395 0.8556 3.4306
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99351 0.08692 114.971 < 2e-16 ***
## category_code_LT01_3_count 0.36927 0.11200 3.297 0.00105 **
## category_code_LT01_4_count 0.82802 0.08660 9.562 < 2e-16 ***
## category_code_LT01_5_count 0.91971 0.06248 14.719 < 2e-16 ***
## category_code_LT01_7_count 0.49994 0.15434 3.239 0.00128 **
## category_code_LT01_8_count -0.17521 0.27390 -0.640 0.52268
## category_code_LT01_13_count 0.02198 0.24460 0.090 0.92845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.624505954250154
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0157 -0.7571 0.0382 0.8594 3.4316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99479 0.08698 114.913 < 2e-16 ***
## category_code_LT01_3_count 0.37118 0.11208 3.312 0.000996 ***
## category_code_LT01_4_count 0.82288 0.08776 9.377 < 2e-16 ***
## category_code_LT01_5_count 0.91762 0.06276 14.620 < 2e-16 ***
## category_code_LT01_7_count 0.49763 0.15360 3.240 0.001277 **
## category_code_LT01_8_count -0.17804 0.27336 -0.651 0.515143
## category_code_LT01_14_count 0.11720 0.32735 0.358 0.720488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.624415559029627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0162 -0.7634 0.0391 0.8547 3.4302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99363 0.08692 114.972 < 2e-16 ***
## category_code_LT01_3_count 0.36792 0.11299 3.256 0.00121 **
## category_code_LT01_4_count 0.82803 0.08652 9.570 < 2e-16 ***
## category_code_LT01_5_count 0.92001 0.06248 14.726 < 2e-16 ***
## category_code_LT01_7_count 0.50211 0.15330 3.275 0.00113 **
## category_code_LT01_8_count -0.17700 0.27338 -0.647 0.51764
## category_code_LT01_15_count 0.07518 0.75294 0.100 0.92051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6289, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.624790070185411
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0162 -0.7718 0.0324 0.8566 3.4305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99412 0.08688 115.034 < 2e-16 ***
## category_code_LT01_3_count 0.35947 0.11281 3.186 0.00153 **
## category_code_LT01_4_count 0.82966 0.08620 9.625 < 2e-16 ***
## category_code_LT01_5_count 0.91931 0.06243 14.725 < 2e-16 ***
## category_code_LT01_7_count 0.50215 0.15314 3.279 0.00112 **
## category_code_LT01_8_count -0.18601 0.27354 -0.680 0.49682
## category_code_LT01_16_count 0.82631 1.16851 0.707 0.47981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.626249700856632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9884 -0.7603 0.0588 0.8760 3.4068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97427 0.08995 110.883 < 2e-16 ***
## category_code_LT01_3_count 0.32946 0.11465 2.874 0.00423 **
## category_code_LT01_4_count 0.81741 0.08629 9.473 < 2e-16 ***
## category_code_LT01_5_count 0.90778 0.06176 14.698 < 2e-16 ***
## category_code_LT01_7_count 0.46752 0.15395 3.037 0.00252 **
## category_code_LT01_9_count 0.34714 0.22742 1.526 0.12754
## category_code_LT01_10_count 0.06416 0.11393 0.563 0.57358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6262
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.631603576987511
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0039 -0.7612 0.0472 0.8846 3.4488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99212 0.08610 116.054 < 2e-16 ***
## category_code_LT01_3_count 0.26106 0.11592 2.252 0.02476 *
## category_code_LT01_4_count 0.70807 0.09470 7.477 3.53e-13 ***
## category_code_LT01_5_count 0.90440 0.06133 14.747 < 2e-16 ***
## category_code_LT01_7_count 0.36671 0.15741 2.330 0.02023 *
## category_code_LT01_9_count 0.33542 0.22490 1.491 0.13650
## category_code_LT01_11_count 0.31682 0.11602 2.731 0.00654 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.367 on 491 degrees of freedom
## Multiple R-squared: 0.6361, Adjusted R-squared: 0.6316
## F-statistic: 143 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.626341979538733
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9982 -0.7178 0.0437 0.8642 3.4564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98691 0.08671 115.182 < 2e-16 ***
## category_code_LT01_3_count 0.33408 0.11342 2.946 0.00338 **
## category_code_LT01_4_count 0.80953 0.08729 9.274 < 2e-16 ***
## category_code_LT01_5_count 0.90378 0.06201 14.574 < 2e-16 ***
## category_code_LT01_7_count 0.47221 0.15357 3.075 0.00222 **
## category_code_LT01_9_count 0.35807 0.22634 1.582 0.11429
## category_code_LT01_12_count 0.13499 0.20385 0.662 0.50816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.626055265796367
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0013 -0.7325 0.0514 0.8492 3.4503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98761 0.08673 115.155 < 2e-16 ***
## category_code_LT01_3_count 0.33952 0.11311 3.002 0.00282 **
## category_code_LT01_4_count 0.81632 0.08670 9.415 < 2e-16 ***
## category_code_LT01_5_count 0.90718 0.06179 14.681 < 2e-16 ***
## category_code_LT01_7_count 0.46864 0.15485 3.026 0.00260 **
## category_code_LT01_9_count 0.36368 0.22704 1.602 0.10984
## category_code_LT01_13_count 0.06067 0.24426 0.248 0.80395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.626052331607035
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0015 -0.7327 0.0420 0.8597 3.4505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98862 0.08680 115.072 < 2e-16 ***
## category_code_LT01_3_count 0.34170 0.11324 3.017 0.00268 **
## category_code_LT01_4_count 0.81459 0.08774 9.284 < 2e-16 ***
## category_code_LT01_5_count 0.90607 0.06208 14.596 < 2e-16 ***
## category_code_LT01_7_count 0.47105 0.15395 3.060 0.00234 **
## category_code_LT01_9_count 0.35572 0.22696 1.567 0.11769
## category_code_LT01_14_count 0.07875 0.32744 0.240 0.81005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.626024814268335
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0019 -0.7358 0.0478 0.8606 3.4495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98784 0.08673 115.154 < 2e-16 ***
## category_code_LT01_3_count 0.33793 0.11416 2.960 0.00322 **
## category_code_LT01_4_count 0.81734 0.08660 9.438 < 2e-16 ***
## category_code_LT01_5_count 0.90773 0.06179 14.690 < 2e-16 ***
## category_code_LT01_7_count 0.47413 0.15369 3.085 0.00215 **
## category_code_LT01_9_count 0.36063 0.22655 1.592 0.11207
## category_code_LT01_15_count 0.11075 0.75171 0.147 0.88293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.626312606097749
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0017 -0.7327 0.0422 0.8624 3.4595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98822 0.08670 115.201 < 2e-16 ***
## category_code_LT01_3_count 0.33160 0.11386 2.912 0.00375 **
## category_code_LT01_4_count 0.81936 0.08627 9.497 < 2e-16 ***
## category_code_LT01_5_count 0.90685 0.06176 14.683 < 2e-16 ***
## category_code_LT01_7_count 0.47409 0.15357 3.087 0.00214 **
## category_code_LT01_9_count 0.35548 0.22643 1.570 0.11707
## category_code_LT01_16_count 0.73684 1.16525 0.632 0.52745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.630314440981374
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9949 -0.7686 0.0344 0.8899 3.3675
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97903 0.08948 111.528 < 2e-16 ***
## category_code_LT01_3_count 0.27065 0.11680 2.317 0.02090 *
## category_code_LT01_4_count 0.71375 0.09477 7.532 2.43e-13 ***
## category_code_LT01_5_count 0.91030 0.06131 14.848 < 2e-16 ***
## category_code_LT01_7_count 0.37934 0.15750 2.409 0.01638 *
## category_code_LT01_10_count 0.08009 0.11278 0.710 0.47796
## category_code_LT01_11_count 0.32338 0.11613 2.785 0.00557 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.369 on 491 degrees of freedom
## Multiple R-squared: 0.6348, Adjusted R-squared: 0.6303
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.624799871607533
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9902 -0.7571 0.0441 0.8854 3.3741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97439 0.09013 110.669 < 2e-16 ***
## category_code_LT01_3_count 0.34750 0.11415 3.044 0.00246 **
## category_code_LT01_4_count 0.81833 0.08726 9.378 < 2e-16 ***
## category_code_LT01_5_count 0.91021 0.06201 14.678 < 2e-16 ***
## category_code_LT01_7_count 0.48888 0.15355 3.184 0.00155 **
## category_code_LT01_10_count 0.07831 0.11369 0.689 0.49128
## category_code_LT01_12_count 0.13305 0.20439 0.651 0.51540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.624486431443256
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9930 -0.7530 0.0503 0.8720 3.3673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97469 0.09016 110.628 < 2e-16 ***
## category_code_LT01_3_count 0.35306 0.11387 3.101 0.00204 **
## category_code_LT01_4_count 0.82615 0.08662 9.538 < 2e-16 ***
## category_code_LT01_5_count 0.91381 0.06178 14.790 < 2e-16 ***
## category_code_LT01_7_count 0.48784 0.15466 3.154 0.00171 **
## category_code_LT01_10_count 0.08069 0.11368 0.710 0.47816
## category_code_LT01_13_count 0.02839 0.24415 0.116 0.90748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.62450557296998
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9939 -0.7527 0.0305 0.8734 3.3721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97644 0.09061 110.107 <2e-16 ***
## category_code_LT01_3_count 0.35510 0.11428 3.107 0.0020 **
## category_code_LT01_4_count 0.82388 0.08776 9.387 <2e-16 ***
## category_code_LT01_5_count 0.91266 0.06212 14.692 <2e-16 ***
## category_code_LT01_7_count 0.48823 0.15385 3.173 0.0016 **
## category_code_LT01_10_count 0.07587 0.11656 0.651 0.5154
## category_code_LT01_14_count 0.06591 0.33568 0.196 0.8444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.624477875052234
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9932 -0.7536 0.0491 0.8671 3.3671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97479 0.09019 110.596 < 2e-16 ***
## category_code_LT01_3_count 0.35253 0.11469 3.074 0.00223 **
## category_code_LT01_4_count 0.82673 0.08654 9.554 < 2e-16 ***
## category_code_LT01_5_count 0.91401 0.06179 14.792 < 2e-16 ***
## category_code_LT01_7_count 0.49022 0.15371 3.189 0.00152 **
## category_code_LT01_10_count 0.08060 0.11390 0.708 0.47951
## category_code_LT01_15_count 0.03645 0.75437 0.048 0.96149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.624804352000112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9934 -0.7515 0.0377 0.8667 3.3692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97556 0.09014 110.672 < 2e-16 ***
## category_code_LT01_3_count 0.34429 0.11462 3.004 0.00280 **
## category_code_LT01_4_count 0.82797 0.08623 9.602 < 2e-16 ***
## category_code_LT01_5_count 0.91315 0.06176 14.786 < 2e-16 ***
## category_code_LT01_7_count 0.49051 0.15354 3.195 0.00149 **
## category_code_LT01_10_count 0.07884 0.11366 0.694 0.48825
## category_code_LT01_16_count 0.76528 1.16761 0.655 0.51250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.629937746435471
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0122 -0.7820 0.0605 0.8761 3.4380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99616 0.08627 115.870 < 2e-16 ***
## category_code_LT01_3_count 0.28531 0.11505 2.480 0.01348 *
## category_code_LT01_4_count 0.71553 0.09483 7.546 2.2e-13 ***
## category_code_LT01_5_count 0.91063 0.06159 14.786 < 2e-16 ***
## category_code_LT01_7_count 0.38721 0.15738 2.460 0.01422 *
## category_code_LT01_11_count 0.32563 0.12054 2.701 0.00714 **
## category_code_LT01_12_count -0.01326 0.21045 -0.063 0.94979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.629936992888347
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0118 -0.7818 0.0600 0.8769 3.4385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99602 0.08625 115.891 < 2e-16 ***
## category_code_LT01_3_count 0.28518 0.11504 2.479 0.0135 *
## category_code_LT01_4_count 0.71499 0.09503 7.524 2.56e-13 ***
## category_code_LT01_5_count 0.91022 0.06135 14.837 < 2e-16 ***
## category_code_LT01_7_count 0.38684 0.15805 2.448 0.0147 *
## category_code_LT01_11_count 0.32343 0.11623 2.783 0.0056 **
## category_code_LT01_13_count 0.01321 0.24242 0.054 0.9566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.629999179107424
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0116 -0.7839 0.0624 0.8768 3.4395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99702 0.08631 115.827 < 2e-16 ***
## category_code_LT01_3_count 0.28682 0.11516 2.491 0.01308 *
## category_code_LT01_4_count 0.71087 0.09601 7.404 5.8e-13 ***
## category_code_LT01_5_count 0.90843 0.06166 14.734 < 2e-16 ***
## category_code_LT01_7_count 0.38478 0.15745 2.444 0.01488 *
## category_code_LT01_11_count 0.32288 0.11621 2.779 0.00567 **
## category_code_LT01_14_count 0.09503 0.32500 0.292 0.77011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6345, Adjusted R-squared: 0.63
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.629935229401434
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0118 -0.7818 0.0582 0.8768 3.4385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99603 0.08625 115.890 < 2e-16 ***
## category_code_LT01_3_count 0.28556 0.11585 2.465 0.01405 *
## category_code_LT01_4_count 0.71549 0.09494 7.537 2.34e-13 ***
## category_code_LT01_5_count 0.91024 0.06135 14.836 < 2e-16 ***
## category_code_LT01_7_count 0.38761 0.15727 2.465 0.01406 *
## category_code_LT01_11_count 0.32373 0.11630 2.784 0.00558 **
## category_code_LT01_15_count -0.01877 0.74803 -0.025 0.97999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6344, Adjusted R-squared: 0.6299
## F-statistic: 142 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.630256420933286
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0118 -0.7810 0.0654 0.8806 3.4389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99646 0.08622 115.944 < 2e-16 ***
## category_code_LT01_3_count 0.27621 0.11581 2.385 0.01746 *
## category_code_LT01_4_count 0.71647 0.09476 7.561 1.99e-13 ***
## category_code_LT01_5_count 0.90950 0.06132 14.831 < 2e-16 ***
## category_code_LT01_7_count 0.38836 0.15706 2.473 0.01375 *
## category_code_LT01_11_count 0.32288 0.11614 2.780 0.00564 **
## category_code_LT01_16_count 0.75728 1.15868 0.654 0.51369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 491 degrees of freedom
## Multiple R-squared: 0.6347, Adjusted R-squared: 0.6303
## F-statistic: 142.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.624446932507703
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0066 -0.7575 0.0511 0.8627 3.4439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99095 0.08689 114.987 < 2e-16 ***
## category_code_LT01_3_count 0.36143 0.11237 3.216 0.00138 **
## category_code_LT01_4_count 0.81880 0.08761 9.346 < 2e-16 ***
## category_code_LT01_5_count 0.90994 0.06205 14.665 < 2e-16 ***
## category_code_LT01_7_count 0.49510 0.15423 3.210 0.00141 **
## category_code_LT01_12_count 0.13746 0.20443 0.672 0.50164
## category_code_LT01_13_count 0.02735 0.24420 0.112 0.91086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.624504782695491
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7502 0.0201 0.8760 3.4447
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99203 0.08695 114.916 < 2e-16 ***
## category_code_LT01_3_count 0.36325 0.11249 3.229 0.00133 **
## category_code_LT01_4_count 0.81510 0.08861 9.199 < 2e-16 ***
## category_code_LT01_5_count 0.90829 0.06232 14.574 < 2e-16 ***
## category_code_LT01_7_count 0.49387 0.15354 3.217 0.00138 **
## category_code_LT01_12_count 0.13327 0.20498 0.650 0.51590
## category_code_LT01_14_count 0.09751 0.32835 0.297 0.76661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.624446545702858
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0068 -0.7563 0.0508 0.8715 3.4435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99106 0.08689 114.987 < 2e-16 ***
## category_code_LT01_3_count 0.35989 0.11339 3.174 0.00160 **
## category_code_LT01_4_count 0.81884 0.08759 9.348 < 2e-16 ***
## category_code_LT01_5_count 0.91019 0.06205 14.668 < 2e-16 ***
## category_code_LT01_7_count 0.49769 0.15323 3.248 0.00124 **
## category_code_LT01_12_count 0.13860 0.20442 0.678 0.49806
## category_code_LT01_15_count 0.08264 0.75308 0.110 0.91267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.624792304203452
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7462 0.0269 0.8727 3.4443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99143 0.08685 115.043 < 2e-16 ***
## category_code_LT01_3_count 0.35186 0.11321 3.108 0.00199 **
## category_code_LT01_4_count 0.82046 0.08725 9.404 < 2e-16 ***
## category_code_LT01_5_count 0.90919 0.06203 14.658 < 2e-16 ***
## category_code_LT01_7_count 0.49749 0.15308 3.250 0.00123 **
## category_code_LT01_12_count 0.13934 0.20427 0.682 0.49547
## category_code_LT01_16_count 0.79552 1.16721 0.682 0.49584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.624195080727112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0097 -0.7611 0.0481 0.8593 3.4401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99301 0.08697 114.898 < 2e-16 ***
## category_code_LT01_3_count 0.36949 0.11211 3.296 0.00105 **
## category_code_LT01_4_count 0.82199 0.08816 9.324 < 2e-16 ***
## category_code_LT01_5_count 0.91153 0.06214 14.669 < 2e-16 ***
## category_code_LT01_7_count 0.49219 0.15469 3.182 0.00156 **
## category_code_LT01_13_count 0.03249 0.24421 0.133 0.89420
## category_code_LT01_14_count 0.11475 0.32747 0.350 0.72619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.624109034152621
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0102 -0.7634 0.0518 0.8566 3.4386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99188 0.08692 114.956 < 2e-16 ***
## category_code_LT01_3_count 0.36623 0.11304 3.240 0.00128 **
## category_code_LT01_4_count 0.82697 0.08696 9.510 < 2e-16 ***
## category_code_LT01_5_count 0.91391 0.06183 14.781 < 2e-16 ***
## category_code_LT01_7_count 0.49656 0.15434 3.217 0.00138 **
## category_code_LT01_13_count 0.03325 0.24469 0.136 0.89196
## category_code_LT01_15_count 0.07679 0.75465 0.102 0.91899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.624454903788067
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0099 -0.7626 0.0480 0.8564 3.4393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99226 0.08688 115.013 < 2e-16 ***
## category_code_LT01_3_count 0.35809 0.11287 3.173 0.00161 **
## category_code_LT01_4_count 0.82845 0.08660 9.567 < 2e-16 ***
## category_code_LT01_5_count 0.91292 0.06180 14.772 < 2e-16 ***
## category_code_LT01_7_count 0.49608 0.15422 3.217 0.00138 **
## category_code_LT01_13_count 0.03767 0.24427 0.154 0.87751
## category_code_LT01_16_count 0.79468 1.16844 0.680 0.49675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.624187298208454
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0100 -0.7628 0.0479 0.8669 3.4397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99311 0.08697 114.897 < 2e-16 ***
## category_code_LT01_3_count 0.36837 0.11312 3.256 0.00121 **
## category_code_LT01_4_count 0.82248 0.08805 9.341 < 2e-16 ***
## category_code_LT01_5_count 0.91183 0.06215 14.672 < 2e-16 ***
## category_code_LT01_7_count 0.49509 0.15369 3.221 0.00136 **
## category_code_LT01_14_count 0.11380 0.32753 0.347 0.72840
## category_code_LT01_15_count 0.06540 0.75327 0.087 0.93085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.624546512264453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0097 -0.7571 0.0445 0.8682 3.4405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99362 0.08694 114.955 < 2e-16 ***
## category_code_LT01_3_count 0.36003 0.11292 3.188 0.00152 **
## category_code_LT01_4_count 0.82349 0.08775 9.384 < 2e-16 ***
## category_code_LT01_5_count 0.91066 0.06211 14.661 < 2e-16 ***
## category_code_LT01_7_count 0.49467 0.15352 3.222 0.00136 **
## category_code_LT01_14_count 0.12414 0.32761 0.379 0.70490
## category_code_LT01_16_count 0.80738 1.16864 0.691 0.48997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.624448399414603
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0103 -0.7632 0.0435 0.8653 3.4388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99240 0.08688 115.012 < 2e-16 ***
## category_code_LT01_3_count 0.35647 0.11394 3.129 0.00186 **
## category_code_LT01_4_count 0.82879 0.08652 9.579 < 2e-16 ***
## category_code_LT01_5_count 0.91327 0.06180 14.777 < 2e-16 ***
## category_code_LT01_7_count 0.49952 0.15322 3.260 0.00119 **
## category_code_LT01_15_count 0.09314 0.75361 0.124 0.90168
## category_code_LT01_16_count 0.79462 1.16884 0.680 0.49693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6244
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.619520593633708
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7599 0.0630 0.8807 3.3649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97034 0.09077 109.844 < 2e-16 ***
## category_code_LT01_3_count 0.33976 0.11565 2.938 0.00346 **
## category_code_LT01_4_count 0.90790 0.08175 11.106 < 2e-16 ***
## category_code_LT01_5_count 0.92271 0.06292 14.665 < 2e-16 ***
## category_code_LT01_8_count -0.16875 0.27518 -0.613 0.54001
## category_code_LT01_9_count 0.41709 0.22851 1.825 0.06856 .
## category_code_LT01_10_count 0.08908 0.11469 0.777 0.43773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6241, Adjusted R-squared: 0.6195
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.627729201792092
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0134 -0.7732 0.0549 0.8857 3.4350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99412 0.08659 115.425 < 2e-16 ***
## category_code_LT01_3_count 0.25552 0.11652 2.193 0.028779 *
## category_code_LT01_4_count 0.75261 0.09334 8.063 5.74e-15 ***
## category_code_LT01_5_count 0.91521 0.06227 14.697 < 2e-16 ***
## category_code_LT01_8_count -0.13903 0.27227 -0.511 0.609833
## category_code_LT01_9_count 0.38617 0.22529 1.714 0.087140 .
## category_code_LT01_11_count 0.38231 0.11302 3.383 0.000775 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6322, Adjusted R-squared: 0.6277
## F-statistic: 140.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.619449863892564
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0097 -0.7777 0.0554 0.8866 3.4373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98817 0.08754 114.101 < 2e-16 ***
## category_code_LT01_3_count 0.34822 0.11439 3.044 0.00246 **
## category_code_LT01_4_count 0.90094 0.08284 10.876 < 2e-16 ***
## category_code_LT01_5_count 0.91853 0.06316 14.544 < 2e-16 ***
## category_code_LT01_8_count -0.17212 0.27533 -0.625 0.53217
## category_code_LT01_9_count 0.43382 0.22732 1.908 0.05692 .
## category_code_LT01_12_count 0.14726 0.20583 0.715 0.47469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.619327998779237
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0119 -0.7781 0.0543 0.8804 3.4341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98858 0.08755 114.094 < 2e-16 ***
## category_code_LT01_3_count 0.35283 0.11407 3.093 0.00209 **
## category_code_LT01_4_count 0.90358 0.08259 10.940 < 2e-16 ***
## category_code_LT01_5_count 0.92101 0.06298 14.625 < 2e-16 ***
## category_code_LT01_8_count -0.15600 0.27565 -0.566 0.57170
## category_code_LT01_9_count 0.44345 0.22774 1.947 0.05208 .
## category_code_LT01_13_count 0.14580 0.24488 0.595 0.55186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6193
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619207238464798
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0130 -0.7711 0.0535 0.8737 3.4336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99065 0.08763 114.006 < 2e-16 ***
## category_code_LT01_3_count 0.35754 0.11419 3.131 0.00185 **
## category_code_LT01_4_count 0.90288 0.08364 10.795 < 2e-16 ***
## category_code_LT01_5_count 0.91965 0.06325 14.540 < 2e-16 ***
## category_code_LT01_8_count -0.16687 0.27528 -0.606 0.54466
## category_code_LT01_9_count 0.42771 0.22804 1.876 0.06130 .
## category_code_LT01_14_count 0.14697 0.32974 0.446 0.65600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.619056283294917
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0134 -0.7782 0.0505 0.8840 3.4320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98906 0.08758 114.061 < 2e-16 ***
## category_code_LT01_3_count 0.35393 0.11512 3.074 0.00223 **
## category_code_LT01_4_count 0.91047 0.08196 11.109 < 2e-16 ***
## category_code_LT01_5_count 0.92252 0.06297 14.650 < 2e-16 ***
## category_code_LT01_8_count -0.16542 0.27532 -0.601 0.54824
## category_code_LT01_9_count 0.43596 0.22758 1.916 0.05600 .
## category_code_LT01_15_count 0.04805 0.75840 0.063 0.94951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.619366929707015
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0135 -0.7766 0.0462 0.8833 3.4320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98957 0.08754 114.110 <2e-16 ***
## category_code_LT01_3_count 0.34622 0.11483 3.015 0.0027 **
## category_code_LT01_4_count 0.91198 0.08169 11.164 <2e-16 ***
## category_code_LT01_5_count 0.92201 0.06293 14.651 <2e-16 ***
## category_code_LT01_8_count -0.17348 0.27550 -0.630 0.5292
## category_code_LT01_9_count 0.43164 0.22741 1.898 0.0583 .
## category_code_LT01_16_count 0.74899 1.17731 0.636 0.5249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.626116277498075
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0009 -0.7624 0.0528 0.8734 3.3331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97700 0.09000 110.858 < 2e-16 ***
## category_code_LT01_3_count 0.26459 0.11745 2.253 0.024719 *
## category_code_LT01_4_count 0.76054 0.09339 8.144 3.19e-15 ***
## category_code_LT01_5_count 0.92192 0.06228 14.802 < 2e-16 ***
## category_code_LT01_8_count -0.12874 0.27277 -0.472 0.637164
## category_code_LT01_10_count 0.10164 0.11312 0.899 0.369350
## category_code_LT01_11_count 0.39248 0.11305 3.472 0.000563 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.61731832266408
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9972 -0.7597 0.0529 0.8807 3.3282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97017 0.09103 109.525 < 2e-16 ***
## category_code_LT01_3_count 0.36282 0.11521 3.149 0.00174 **
## category_code_LT01_4_count 0.91512 0.08263 11.075 < 2e-16 ***
## category_code_LT01_5_count 0.92639 0.06320 14.657 < 2e-16 ***
## category_code_LT01_8_count -0.16084 0.27602 -0.583 0.56036
## category_code_LT01_10_count 0.10782 0.11448 0.942 0.34675
## category_code_LT01_12_count 0.14411 0.20653 0.698 0.48564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.617097549462716
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9995 -0.7600 0.0505 0.8644 3.3232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97047 0.09106 109.498 < 2e-16 ***
## category_code_LT01_3_count 0.36815 0.11492 3.204 0.00145 **
## category_code_LT01_4_count 0.91958 0.08230 11.174 < 2e-16 ***
## category_code_LT01_5_count 0.92927 0.06302 14.746 < 2e-16 ***
## category_code_LT01_8_count -0.14687 0.27641 -0.531 0.59542
## category_code_LT01_10_count 0.10917 0.11449 0.954 0.34079
## category_code_LT01_13_count 0.11065 0.24528 0.451 0.65209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.617051074202533
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0018 -0.7635 0.0517 0.8577 3.3309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97389 0.09152 108.979 < 2e-16 ***
## category_code_LT01_3_count 0.37260 0.11531 3.231 0.00131 **
## category_code_LT01_4_count 0.91794 0.08346 10.998 < 2e-16 ***
## category_code_LT01_5_count 0.92769 0.06334 14.646 < 2e-16 ***
## category_code_LT01_8_count -0.15539 0.27597 -0.563 0.57365
## category_code_LT01_10_count 0.10063 0.11749 0.857 0.39213
## category_code_LT01_14_count 0.12840 0.33847 0.379 0.70459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.616942298759624
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9999 -0.7598 0.0420 0.8649 3.3203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97028 0.09110 109.443 < 2e-16 ***
## category_code_LT01_3_count 0.36988 0.11574 3.196 0.00148 **
## category_code_LT01_4_count 0.92519 0.08170 11.324 < 2e-16 ***
## category_code_LT01_5_count 0.93012 0.06302 14.760 < 2e-16 ***
## category_code_LT01_8_count -0.15405 0.27600 -0.558 0.57700
## category_code_LT01_10_count 0.11116 0.11468 0.969 0.33286
## category_code_LT01_15_count -0.05072 0.76139 -0.067 0.94692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6169
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.61727655736165
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0007 -0.7625 0.0458 0.8756 3.3224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97137 0.09105 109.519 < 2e-16 ***
## category_code_LT01_3_count 0.36001 0.11568 3.112 0.00197 **
## category_code_LT01_4_count 0.92586 0.08148 11.364 < 2e-16 ***
## category_code_LT01_5_count 0.92973 0.06297 14.764 < 2e-16 ***
## category_code_LT01_8_count -0.16278 0.27618 -0.589 0.55586
## category_code_LT01_10_count 0.10866 0.11446 0.949 0.34292
## category_code_LT01_16_count 0.77708 1.18057 0.658 0.51070
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.625526500979463
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0234 -0.7704 0.0462 0.8723 3.4218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99887 0.08682 115.172 < 2e-16 ***
## category_code_LT01_3_count 0.28319 0.11577 2.446 0.014790 *
## category_code_LT01_4_count 0.76413 0.09340 8.181 2.43e-15 ***
## category_code_LT01_5_count 0.92283 0.06253 14.758 < 2e-16 ***
## category_code_LT01_8_count -0.12165 0.27319 -0.445 0.656289
## category_code_LT01_11_count 0.40038 0.11721 3.416 0.000688 ***
## category_code_LT01_12_count -0.03829 0.21157 -0.181 0.856441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.625567218400191
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0219 -0.7677 0.0487 0.8727 3.4240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99838 0.08680 115.190 < 2e-16 ***
## category_code_LT01_3_count 0.28270 0.11576 2.442 0.014950 *
## category_code_LT01_4_count 0.76122 0.09381 8.114 3.96e-15 ***
## category_code_LT01_5_count 0.92134 0.06236 14.775 < 2e-16 ***
## category_code_LT01_8_count -0.11926 0.27335 -0.436 0.662817
## category_code_LT01_11_count 0.39300 0.11328 3.469 0.000568 ***
## category_code_LT01_13_count 0.07127 0.24281 0.294 0.769243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.625660914632894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0220 -0.7645 0.0512 0.8635 3.4248
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00011 0.08685 115.142 < 2e-16 ***
## category_code_LT01_3_count 0.28551 0.11588 2.464 0.014087 *
## category_code_LT01_4_count 0.75625 0.09484 7.974 1.09e-14 ***
## category_code_LT01_5_count 0.91901 0.06264 14.670 < 2e-16 ***
## category_code_LT01_8_count -0.12589 0.27291 -0.461 0.644813
## category_code_LT01_11_count 0.39278 0.11318 3.470 0.000566 ***
## category_code_LT01_14_count 0.14918 0.32625 0.457 0.647694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.62551334990949
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0223 -0.7701 0.0444 0.8737 3.4234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99851 0.08681 115.183 < 2e-16 ***
## category_code_LT01_3_count 0.28463 0.11658 2.442 0.014978 *
## category_code_LT01_4_count 0.76441 0.09350 8.176 2.52e-15 ***
## category_code_LT01_5_count 0.92171 0.06235 14.782 < 2e-16 ***
## category_code_LT01_8_count -0.12349 0.27294 -0.452 0.651158
## category_code_LT01_11_count 0.39530 0.11318 3.493 0.000521 ***
## category_code_LT01_15_count -0.09366 0.75185 -0.125 0.900914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.625831575980941
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0227 -0.7665 0.0446 0.8761 3.4231
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99909 0.08677 115.237 < 2e-16 ***
## category_code_LT01_3_count 0.27388 0.11652 2.350 0.019148 *
## category_code_LT01_4_count 0.76512 0.09337 8.195 2.2e-15 ***
## category_code_LT01_5_count 0.92144 0.06231 14.788 < 2e-16 ***
## category_code_LT01_8_count -0.13245 0.27313 -0.485 0.627942
## category_code_LT01_11_count 0.39410 0.11307 3.485 0.000535 ***
## category_code_LT01_16_count 0.76798 1.16693 0.658 0.510770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616790315718456
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0190 -0.7704 0.0485 0.8686 3.4252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99274 0.08781 113.802 < 2e-16 ***
## category_code_LT01_3_count 0.38162 0.11337 3.366 0.000822 ***
## category_code_LT01_4_count 0.91404 0.08334 10.968 < 2e-16 ***
## category_code_LT01_5_count 0.92537 0.06328 14.624 < 2e-16 ***
## category_code_LT01_8_count -0.14862 0.27665 -0.537 0.591363
## category_code_LT01_12_count 0.14827 0.20663 0.718 0.473385
## category_code_LT01_13_count 0.11225 0.24538 0.457 0.647534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616847535083698
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0195 -0.7680 0.0535 0.8601 3.4256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99488 0.08787 113.744 < 2e-16 ***
## category_code_LT01_3_count 0.38542 0.11347 3.397 0.000737 ***
## category_code_LT01_4_count 0.90983 0.08443 10.776 < 2e-16 ***
## category_code_LT01_5_count 0.92301 0.06354 14.527 < 2e-16 ***
## category_code_LT01_8_count -0.15799 0.27617 -0.572 0.567538
## category_code_LT01_12_count 0.14239 0.20718 0.687 0.492213
## category_code_LT01_14_count 0.17589 0.33085 0.532 0.595218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6168
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616627060830846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0199 -0.7747 0.0420 0.8709 3.4239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.993001 0.087827 113.780 < 2e-16 ***
## category_code_LT01_3_count 0.382453 0.114391 3.343 0.000891 ***
## category_code_LT01_4_count 0.919145 0.082840 11.095 < 2e-16 ***
## category_code_LT01_5_count 0.926280 0.063278 14.638 < 2e-16 ***
## category_code_LT01_8_count -0.156133 0.276237 -0.565 0.572187
## category_code_LT01_12_count 0.151164 0.206647 0.732 0.464820
## category_code_LT01_15_count 0.007816 0.760522 0.010 0.991804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617000468207576
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0201 -0.7761 0.0344 0.8770 3.4240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99353 0.08779 113.840 < 2e-16 ***
## category_code_LT01_3_count 0.37272 0.11422 3.263 0.00118 **
## category_code_LT01_4_count 0.92013 0.08256 11.145 < 2e-16 ***
## category_code_LT01_5_count 0.92569 0.06324 14.639 < 2e-16 ***
## category_code_LT01_8_count -0.16532 0.27641 -0.598 0.55006
## category_code_LT01_12_count 0.15267 0.20650 0.739 0.46006
## category_code_LT01_16_count 0.81695 1.18063 0.692 0.48929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616657955405408
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0220 -0.7756 0.0512 0.8539 3.4222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99563 0.08788 113.739 < 2e-16 ***
## category_code_LT01_3_count 0.39110 0.11308 3.459 0.00059 ***
## category_code_LT01_4_count 0.91288 0.08432 10.826 < 2e-16 ***
## category_code_LT01_5_count 0.92543 0.06339 14.599 < 2e-16 ***
## category_code_LT01_8_count -0.14382 0.27654 -0.520 0.60325
## category_code_LT01_13_count 0.11748 0.24531 0.479 0.63223
## category_code_LT01_14_count 0.19381 0.32989 0.587 0.55714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616388911787313
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0226 -0.7832 0.0468 0.8575 3.4200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99362 0.08785 113.761 < 2e-16 ***
## category_code_LT01_3_count 0.38809 0.11404 3.403 0.000721 ***
## category_code_LT01_4_count 0.92371 0.08254 11.191 < 2e-16 ***
## category_code_LT01_5_count 0.92931 0.06309 14.729 < 2e-16 ***
## category_code_LT01_8_count -0.14132 0.27661 -0.511 0.609647
## category_code_LT01_13_count 0.11792 0.24591 0.480 0.631774
## category_code_LT01_15_count 0.01779 0.76213 0.023 0.981385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.616771024009328
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0227 -0.7834 0.0375 0.8612 3.4201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99415 0.08781 113.822 < 2e-16 ***
## category_code_LT01_3_count 0.37845 0.11387 3.323 0.000956 ***
## category_code_LT01_4_count 0.92463 0.08222 11.246 < 2e-16 ***
## category_code_LT01_5_count 0.92868 0.06305 14.729 < 2e-16 ***
## category_code_LT01_8_count -0.15017 0.27676 -0.543 0.587654
## category_code_LT01_13_count 0.12327 0.24541 0.502 0.615670
## category_code_LT01_16_count 0.82720 1.18157 0.700 0.484208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.61647914413091
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0229 -0.7794 0.0482 0.8549 3.4209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99591 0.08790 113.715 < 2e-16 ***
## category_code_LT01_3_count 0.39253 0.11408 3.441 0.000629 ***
## category_code_LT01_4_count 0.91859 0.08373 10.971 < 2e-16 ***
## category_code_LT01_5_count 0.92642 0.06339 14.613 < 2e-16 ***
## category_code_LT01_8_count -0.15147 0.27615 -0.549 0.583585
## category_code_LT01_14_count 0.19399 0.33002 0.588 0.556928
## category_code_LT01_15_count -0.01337 0.76055 -0.018 0.985980
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616872808023608
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0231 -0.7777 0.0392 0.8548 3.4210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99659 0.08786 113.778 < 2e-16 ***
## category_code_LT01_3_count 0.38232 0.11389 3.357 0.000849 ***
## category_code_LT01_4_count 0.91895 0.08348 11.008 < 2e-16 ***
## category_code_LT01_5_count 0.92570 0.06335 14.613 < 2e-16 ***
## category_code_LT01_8_count -0.16104 0.27632 -0.583 0.560292
## category_code_LT01_14_count 0.20426 0.33012 0.619 0.536379
## category_code_LT01_16_count 0.83975 1.18191 0.711 0.477730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6169
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616574491037919
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0238 -0.7840 0.0348 0.8584 3.4186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99445 0.08783 113.795 < 2e-16 ***
## category_code_LT01_3_count 0.37956 0.11493 3.302 0.00103 **
## category_code_LT01_4_count 0.93036 0.08163 11.397 < 2e-16 ***
## category_code_LT01_5_count 0.92981 0.06305 14.747 < 2e-16 ***
## category_code_LT01_8_count -0.15809 0.27640 -0.572 0.56762
## category_code_LT01_15_count 0.01735 0.76110 0.023 0.98182
## category_code_LT01_16_count 0.80863 1.18239 0.684 0.49437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.62792530434044
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9918 -0.7574 0.0499 0.9031 3.3686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97565 0.08976 111.142 < 2e-16 ***
## category_code_LT01_3_count 0.24055 0.11798 2.039 0.041985 *
## category_code_LT01_4_count 0.74942 0.09335 8.028 7.37e-15 ***
## category_code_LT01_5_count 0.91070 0.06157 14.790 < 2e-16 ***
## category_code_LT01_9_count 0.36568 0.22631 1.616 0.106762
## category_code_LT01_10_count 0.08176 0.11342 0.721 0.471324
## category_code_LT01_11_count 0.38264 0.11295 3.388 0.000762 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6324, Adjusted R-squared: 0.6279
## F-statistic: 140.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.619574089308694
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9866 -0.7583 0.0565 0.8970 3.3829
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96873 0.09074 109.864 < 2e-16 ***
## category_code_LT01_3_count 0.33298 0.11596 2.871 0.00426 **
## category_code_LT01_4_count 0.89838 0.08289 10.838 < 2e-16 ***
## category_code_LT01_5_count 0.91320 0.06250 14.611 < 2e-16 ***
## category_code_LT01_9_count 0.41194 0.22840 1.804 0.07191 .
## category_code_LT01_10_count 0.08519 0.11474 0.742 0.45816
## category_code_LT01_12_count 0.13731 0.20580 0.667 0.50496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6196
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.61951006840303
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9889 -0.7587 0.0682 0.8852 3.3747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96916 0.09074 109.860 < 2e-16 ***
## category_code_LT01_3_count 0.33717 0.11567 2.915 0.00372 **
## category_code_LT01_4_count 0.90029 0.08264 10.895 < 2e-16 ***
## category_code_LT01_5_count 0.91593 0.06227 14.710 < 2e-16 ***
## category_code_LT01_9_count 0.42188 0.22889 1.843 0.06590 .
## category_code_LT01_10_count 0.08552 0.11475 0.745 0.45648
## category_code_LT01_13_count 0.14725 0.24459 0.602 0.54742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6241, Adjusted R-squared: 0.6195
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.619288196721725
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9908 -0.7595 0.0641 0.8831 3.3694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97154 0.09123 109.304 < 2e-16 ***
## category_code_LT01_3_count 0.34168 0.11614 2.942 0.00342 **
## category_code_LT01_4_count 0.90273 0.08363 10.794 < 2e-16 ***
## category_code_LT01_5_count 0.91524 0.06260 14.620 < 2e-16 ***
## category_code_LT01_9_count 0.40959 0.22882 1.790 0.07407 .
## category_code_LT01_10_count 0.08079 0.11759 0.687 0.49241
## category_code_LT01_14_count 0.09323 0.33796 0.276 0.78277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6193
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.619229235698052
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9896 -0.7586 0.0648 0.8870 3.3695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.969033 0.090801 109.789 < 2e-16 ***
## category_code_LT01_3_count 0.338695 0.116573 2.905 0.00383 **
## category_code_LT01_4_count 0.907523 0.082014 11.066 < 2e-16 ***
## category_code_LT01_5_count 0.917061 0.062277 14.726 < 2e-16 ***
## category_code_LT01_9_count 0.413106 0.228715 1.806 0.07150 .
## category_code_LT01_10_count 0.087869 0.114965 0.764 0.44505
## category_code_LT01_15_count 0.005749 0.759807 0.008 0.99397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.619497306193294
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9899 -0.7592 0.0523 0.8877 3.3843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96981 0.09076 109.854 < 2e-16 ***
## category_code_LT01_3_count 0.33101 0.11640 2.844 0.00465 **
## category_code_LT01_4_count 0.90863 0.08177 11.112 < 2e-16 ***
## category_code_LT01_5_count 0.91641 0.06225 14.722 < 2e-16 ***
## category_code_LT01_9_count 0.40970 0.22849 1.793 0.07358 .
## category_code_LT01_10_count 0.08622 0.11472 0.752 0.45265
## category_code_LT01_16_count 0.69183 1.17618 0.588 0.55667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6241, Adjusted R-squared: 0.6195
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.627559189763482
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0099 -0.7687 0.0535 0.8939 3.4399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99318 0.08659 115.412 < 2e-16 ***
## category_code_LT01_3_count 0.25448 0.11653 2.184 0.029447 *
## category_code_LT01_4_count 0.75194 0.09336 8.054 6.1e-15 ***
## category_code_LT01_5_count 0.91156 0.06184 14.740 < 2e-16 ***
## category_code_LT01_9_count 0.38217 0.22523 1.697 0.090368 .
## category_code_LT01_11_count 0.38978 0.11704 3.330 0.000933 ***
## category_code_LT01_12_count -0.04027 0.21080 -0.191 0.848562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6321, Adjusted R-squared: 0.6276
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.62767205634366
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0080 -0.7536 0.0603 0.8891 3.4425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99255 0.08656 115.442 < 2e-16 ***
## category_code_LT01_3_count 0.25346 0.11651 2.175 0.030072 *
## category_code_LT01_4_count 0.74764 0.09379 7.971 1.11e-14 ***
## category_code_LT01_5_count 0.90979 0.06162 14.765 < 2e-16 ***
## category_code_LT01_9_count 0.38871 0.22566 1.723 0.085602 .
## category_code_LT01_11_count 0.38100 0.11319 3.366 0.000822 ***
## category_code_LT01_13_count 0.10429 0.24224 0.431 0.667001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6322, Adjusted R-squared: 0.6277
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.627612120519247
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0086 -0.7549 0.0597 0.8994 3.4425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99399 0.08663 115.359 < 2e-16 ***
## category_code_LT01_3_count 0.25637 0.11671 2.197 0.028509 *
## category_code_LT01_4_count 0.74636 0.09473 7.879 2.14e-14 ***
## category_code_LT01_5_count 0.90850 0.06191 14.674 < 2e-16 ***
## category_code_LT01_9_count 0.37697 0.22583 1.669 0.095694 .
## category_code_LT01_11_count 0.38268 0.11305 3.385 0.000769 ***
## category_code_LT01_14_count 0.10637 0.32625 0.326 0.744539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6321, Adjusted R-squared: 0.6276
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.627534513150087
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7604 0.0583 0.8965 3.4415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99282 0.08657 115.426 < 2e-16 ***
## category_code_LT01_3_count 0.25505 0.11741 2.172 0.030304 *
## category_code_LT01_4_count 0.75193 0.09347 8.045 6.54e-15 ***
## category_code_LT01_5_count 0.91044 0.06162 14.774 < 2e-16 ***
## category_code_LT01_9_count 0.38187 0.22540 1.694 0.090868 .
## category_code_LT01_11_count 0.38420 0.11307 3.398 0.000734 ***
## category_code_LT01_15_count -0.04728 0.75036 -0.063 0.949789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.632, Adjusted R-squared: 0.6275
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.62779664583869
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7558 0.0571 0.9003 3.4416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99327 0.08654 115.470 < 2e-16 ***
## category_code_LT01_3_count 0.24622 0.11725 2.100 0.036236 *
## category_code_LT01_4_count 0.75281 0.09334 8.066 5.63e-15 ***
## category_code_LT01_5_count 0.90990 0.06159 14.772 < 2e-16 ***
## category_code_LT01_9_count 0.37881 0.22523 1.682 0.093231 .
## category_code_LT01_11_count 0.38350 0.11296 3.395 0.000742 ***
## category_code_LT01_16_count 0.68778 1.16294 0.591 0.554515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6323, Adjusted R-squared: 0.6278
## F-statistic: 140.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.619434154097385
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0032 -0.7758 0.0544 0.8913 3.4465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98625 0.08751 114.122 < 2e-16 ***
## category_code_LT01_3_count 0.34534 0.11442 3.018 0.00268 **
## category_code_LT01_4_count 0.89368 0.08368 10.680 < 2e-16 ***
## category_code_LT01_5_count 0.91187 0.06253 14.582 < 2e-16 ***
## category_code_LT01_9_count 0.43798 0.22764 1.924 0.05494 .
## category_code_LT01_12_count 0.13917 0.20580 0.676 0.49919
## category_code_LT01_13_count 0.14887 0.24456 0.609 0.54300
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.619261371162911
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0041 -0.7776 0.0565 0.8892 3.4461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98803 0.08760 114.016 < 2e-16 ***
## category_code_LT01_3_count 0.34979 0.11460 3.052 0.00239 **
## category_code_LT01_4_count 0.89437 0.08457 10.576 < 2e-16 ***
## category_code_LT01_5_count 0.91062 0.06280 14.500 < 2e-16 ***
## category_code_LT01_9_count 0.42287 0.22792 1.855 0.06415 .
## category_code_LT01_12_count 0.13650 0.20640 0.661 0.50872
## category_code_LT01_14_count 0.12703 0.33073 0.384 0.70107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6193
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.619151224481821
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0043 -0.7762 0.0509 0.8925 3.4449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98664 0.08754 114.084 < 2e-16 ***
## category_code_LT01_3_count 0.34607 0.11551 2.996 0.00287 **
## category_code_LT01_4_count 0.90037 0.08315 10.828 < 2e-16 ***
## category_code_LT01_5_count 0.91298 0.06255 14.597 < 2e-16 ***
## category_code_LT01_9_count 0.43011 0.22745 1.891 0.05922 .
## category_code_LT01_12_count 0.14313 0.20585 0.695 0.48718
## category_code_LT01_15_count 0.05618 0.75849 0.074 0.94099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.619439122554164
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0042 -0.7766 0.0386 0.8964 3.4479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98704 0.08751 114.130 < 2e-16 ***
## category_code_LT01_3_count 0.33875 0.11521 2.940 0.00343 **
## category_code_LT01_4_count 0.90183 0.08285 10.885 < 2e-16 ***
## category_code_LT01_5_count 0.91219 0.06252 14.590 < 2e-16 ***
## category_code_LT01_9_count 0.42563 0.22730 1.873 0.06173 .
## category_code_LT01_12_count 0.14397 0.20572 0.700 0.48437
## category_code_LT01_16_count 0.72198 1.17595 0.614 0.53953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.619226459781646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0064 -0.7752 0.0647 0.8783 3.4429
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98868 0.08759 114.034 < 2e-16 ***
## category_code_LT01_3_count 0.35421 0.11422 3.101 0.00204 **
## category_code_LT01_4_count 0.89506 0.08452 10.590 < 2e-16 ***
## category_code_LT01_5_count 0.91297 0.06261 14.582 < 2e-16 ***
## category_code_LT01_9_count 0.43233 0.22836 1.893 0.05891 .
## category_code_LT01_13_count 0.15316 0.24453 0.626 0.53139
## category_code_LT01_14_count 0.14344 0.32971 0.435 0.66372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.619087461142068
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0069 -0.7745 0.0622 0.8850 3.4412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98716 0.08754 114.090 <2e-16 ***
## category_code_LT01_3_count 0.35006 0.11519 3.039 0.0025 **
## category_code_LT01_4_count 0.90212 0.08293 10.878 <2e-16 ***
## category_code_LT01_5_count 0.91585 0.06231 14.698 <2e-16 ***
## category_code_LT01_9_count 0.44086 0.22792 1.934 0.0537 .
## category_code_LT01_13_count 0.15527 0.24513 0.633 0.5268
## category_code_LT01_15_count 0.07609 0.76007 0.100 0.9203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.619386632842852
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0067 -0.7746 0.0600 0.8838 3.4417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98756 0.08750 114.138 < 2e-16 ***
## category_code_LT01_3_count 0.34292 0.11489 2.985 0.00298 **
## category_code_LT01_4_count 0.90366 0.08259 10.942 < 2e-16 ***
## category_code_LT01_5_count 0.91500 0.06229 14.690 < 2e-16 ***
## category_code_LT01_9_count 0.43627 0.22771 1.916 0.05596 .
## category_code_LT01_13_count 0.15890 0.24462 0.650 0.51626
## category_code_LT01_16_count 0.74044 1.17668 0.629 0.52947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.618924089660997
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7672 0.0549 0.8814 3.4411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98909 0.08763 113.995 < 2e-16 ***
## category_code_LT01_3_count 0.35559 0.11530 3.084 0.00216 **
## category_code_LT01_4_count 0.90236 0.08389 10.756 < 2e-16 ***
## category_code_LT01_5_count 0.91419 0.06263 14.597 < 2e-16 ***
## category_code_LT01_9_count 0.42402 0.22819 1.858 0.06373 .
## category_code_LT01_14_count 0.14400 0.32990 0.436 0.66267
## category_code_LT01_15_count 0.03702 0.75865 0.049 0.96110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6235, Adjusted R-squared: 0.6189
## F-statistic: 135.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.619227887043194
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0075 -0.7668 0.0562 0.8821 3.4416
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98964 0.08760 114.042 < 2e-16 ***
## category_code_LT01_3_count 0.34788 0.11496 3.026 0.00261 **
## category_code_LT01_4_count 0.90321 0.08364 10.798 < 2e-16 ***
## category_code_LT01_5_count 0.91325 0.06260 14.589 < 2e-16 ***
## category_code_LT01_9_count 0.41916 0.22804 1.838 0.06665 .
## category_code_LT01_14_count 0.15378 0.33005 0.466 0.64147
## category_code_LT01_16_count 0.73920 1.17745 0.628 0.53043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.619064973528135
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0080 -0.7682 0.0485 0.8850 3.4399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98799 0.08754 114.095 < 2e-16 ***
## category_code_LT01_3_count 0.34408 0.11601 2.966 0.00316 **
## category_code_LT01_4_count 0.91102 0.08197 11.114 < 2e-16 ***
## category_code_LT01_5_count 0.91634 0.06230 14.709 < 2e-16 ***
## category_code_LT01_9_count 0.42808 0.22754 1.881 0.06052 .
## category_code_LT01_15_count 0.06357 0.75906 0.084 0.93329
## category_code_LT01_16_count 0.71841 1.17757 0.610 0.54209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6237, Adjusted R-squared: 0.6191
## F-statistic: 135.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.625986674668341
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9979 -0.7595 0.0498 0.8764 3.3376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97623 0.08999 110.854 < 2e-16 ***
## category_code_LT01_3_count 0.26349 0.11744 2.243 0.025310 *
## category_code_LT01_4_count 0.75990 0.09339 8.137 3.36e-15 ***
## category_code_LT01_5_count 0.91875 0.06182 14.860 < 2e-16 ***
## category_code_LT01_10_count 0.10139 0.11317 0.896 0.370738
## category_code_LT01_11_count 0.40090 0.11707 3.424 0.000668 ***
## category_code_LT01_12_count -0.04844 0.21134 -0.229 0.818814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626012946165989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9966 -0.7540 0.0545 0.8783 3.3415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97605 0.08999 110.862 < 2e-16 ***
## category_code_LT01_3_count 0.26326 0.11744 2.242 0.02543 *
## category_code_LT01_4_count 0.75699 0.09379 8.071 5.39e-15 ***
## category_code_LT01_5_count 0.91709 0.06160 14.887 < 2e-16 ***
## category_code_LT01_10_count 0.09966 0.11315 0.881 0.37887
## category_code_LT01_11_count 0.39205 0.11320 3.463 0.00058 ***
## category_code_LT01_13_count 0.07150 0.24236 0.295 0.76812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.625996567338295
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9980 -0.7490 0.0574 0.8807 3.3470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97831 0.09043 110.339 < 2e-16 ***
## category_code_LT01_3_count 0.26595 0.11791 2.256 0.024538 *
## category_code_LT01_4_count 0.75541 0.09478 7.970 1.12e-14 ***
## category_code_LT01_5_count 0.91581 0.06195 14.784 < 2e-16 ***
## category_code_LT01_10_count 0.09386 0.11610 0.808 0.419217
## category_code_LT01_11_count 0.39290 0.11310 3.474 0.000558 ***
## category_code_LT01_14_count 0.08567 0.33468 0.256 0.798084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.62597212977238
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9963 -0.7613 0.0506 0.8804 3.3394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97564 0.09001 110.824 < 2e-16 ***
## category_code_LT01_3_count 0.26556 0.11812 2.248 0.024999 *
## category_code_LT01_4_count 0.76034 0.09348 8.134 3.43e-15 ***
## category_code_LT01_5_count 0.91722 0.06161 14.887 < 2e-16 ***
## category_code_LT01_10_count 0.10181 0.11332 0.898 0.369389
## category_code_LT01_11_count 0.39457 0.11309 3.489 0.000529 ***
## category_code_LT01_15_count -0.13764 0.75272 -0.183 0.854991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.626231234331713
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9972 -0.7543 0.0552 0.8798 3.3422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97682 0.08997 110.891 < 2e-16 ***
## category_code_LT01_3_count 0.25513 0.11815 2.159 0.031309 *
## category_code_LT01_4_count 0.76074 0.09337 8.148 3.1e-15 ***
## category_code_LT01_5_count 0.91680 0.06158 14.888 < 2e-16 ***
## category_code_LT01_10_count 0.09864 0.11312 0.872 0.383624
## category_code_LT01_11_count 0.39338 0.11300 3.481 0.000544 ***
## category_code_LT01_16_count 0.71255 1.16541 0.611 0.541207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6262
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617222329989916
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9919 -0.7586 0.0538 0.8803 3.3388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96903 0.09102 109.529 < 2e-16 ***
## category_code_LT01_3_count 0.36114 0.11522 3.134 0.00182 **
## category_code_LT01_4_count 0.90976 0.08338 10.911 < 2e-16 ***
## category_code_LT01_5_count 0.92038 0.06256 14.713 < 2e-16 ***
## category_code_LT01_10_count 0.10515 0.11452 0.918 0.35897
## category_code_LT01_12_count 0.13737 0.20651 0.665 0.50624
## category_code_LT01_13_count 0.11391 0.24490 0.465 0.64205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.61713560644993
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9938 -0.7615 0.0451 0.8805 3.3453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97189 0.09149 108.994 < 2e-16 ***
## category_code_LT01_3_count 0.36513 0.11567 3.157 0.00169 **
## category_code_LT01_4_count 0.90939 0.08440 10.775 < 2e-16 ***
## category_code_LT01_5_count 0.91897 0.06287 14.616 < 2e-16 ***
## category_code_LT01_10_count 0.09807 0.11748 0.835 0.40422
## category_code_LT01_12_count 0.13504 0.20702 0.652 0.51451
## category_code_LT01_14_count 0.10999 0.33933 0.324 0.74597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617055854480798
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9921 -0.7584 0.0526 0.8822 3.3364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96880 0.09106 109.474 < 2e-16 ***
## category_code_LT01_3_count 0.36255 0.11607 3.123 0.00189 **
## category_code_LT01_4_count 0.91526 0.08289 11.042 < 2e-16 ***
## category_code_LT01_5_count 0.92097 0.06257 14.719 < 2e-16 ***
## category_code_LT01_10_count 0.10698 0.11472 0.933 0.35152
## category_code_LT01_12_count 0.13967 0.20655 0.676 0.49924
## category_code_LT01_15_count -0.04022 0.76152 -0.053 0.95790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617370998948112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9925 -0.7593 0.0530 0.8817 3.3389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96977 0.09101 109.547 < 2e-16 ***
## category_code_LT01_3_count 0.35302 0.11601 3.043 0.00247 **
## category_code_LT01_4_count 0.91585 0.08263 11.083 < 2e-16 ***
## category_code_LT01_5_count 0.92023 0.06254 14.714 < 2e-16 ***
## category_code_LT01_10_count 0.10454 0.11450 0.913 0.36170
## category_code_LT01_12_count 0.14131 0.20641 0.685 0.49392
## category_code_LT01_16_count 0.75244 1.17916 0.638 0.52370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.61698782364337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9965 -0.7574 0.0621 0.8704 3.3417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97276 0.09150 108.990 < 2e-16 ***
## category_code_LT01_3_count 0.37061 0.11531 3.214 0.00139 **
## category_code_LT01_4_count 0.91196 0.08428 10.821 < 2e-16 ***
## category_code_LT01_5_count 0.92167 0.06268 14.704 < 2e-16 ***
## category_code_LT01_10_count 0.09788 0.11753 0.833 0.40536
## category_code_LT01_13_count 0.11894 0.24489 0.486 0.62740
## category_code_LT01_14_count 0.12736 0.33849 0.376 0.70688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616878540058993
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9947 -0.7588 0.0530 0.8684 3.3312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96924 0.09108 109.453 < 2e-16 ***
## category_code_LT01_3_count 0.36755 0.11576 3.175 0.00159 **
## category_code_LT01_4_count 0.91905 0.08257 11.130 < 2e-16 ***
## category_code_LT01_5_count 0.92417 0.06233 14.826 < 2e-16 ***
## category_code_LT01_10_count 0.10816 0.11473 0.943 0.34625
## category_code_LT01_13_count 0.11767 0.24547 0.479 0.63191
## category_code_LT01_15_count -0.02939 0.76314 -0.039 0.96929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6169
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617205993790698
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7598 0.0524 0.8694 3.3337
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97021 0.09103 109.529 < 2e-16 ***
## category_code_LT01_3_count 0.35808 0.11570 3.095 0.00208 **
## category_code_LT01_4_count 0.91954 0.08228 11.176 < 2e-16 ***
## category_code_LT01_5_count 0.92341 0.06230 14.821 < 2e-16 ***
## category_code_LT01_10_count 0.10573 0.11450 0.923 0.35625
## category_code_LT01_13_count 0.12416 0.24498 0.507 0.61253
## category_code_LT01_16_count 0.76619 1.18016 0.649 0.51649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6172
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616807853029558
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9967 -0.7616 0.0558 0.8699 3.3389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97246 0.09155 108.934 < 2e-16 ***
## category_code_LT01_3_count 0.37238 0.11614 3.206 0.00143 **
## category_code_LT01_4_count 0.91802 0.08369 10.970 < 2e-16 ***
## category_code_LT01_5_count 0.92234 0.06269 14.712 < 2e-16 ***
## category_code_LT01_10_count 0.10004 0.11771 0.850 0.39580
## category_code_LT01_14_count 0.12629 0.33856 0.373 0.70929
## category_code_LT01_15_count -0.05482 0.76150 -0.072 0.94264
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617134112324858
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9974 -0.7626 0.0506 0.8706 3.3423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97380 0.09150 109.002 < 2e-16 ***
## category_code_LT01_3_count 0.36279 0.11603 3.127 0.00187 **
## category_code_LT01_4_count 0.91803 0.08346 11.000 < 2e-16 ***
## category_code_LT01_5_count 0.92144 0.06267 14.704 < 2e-16 ***
## category_code_LT01_10_count 0.09657 0.11755 0.822 0.41171
## category_code_LT01_14_count 0.13748 0.33886 0.406 0.68513
## category_code_LT01_16_count 0.76865 1.18101 0.651 0.51545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617007127736759
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9954 -0.7595 0.0443 0.8789 3.3309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96997 0.09108 109.467 < 2e-16 ***
## category_code_LT01_3_count 0.35979 0.11660 3.086 0.00214 **
## category_code_LT01_4_count 0.92558 0.08170 11.329 < 2e-16 ***
## category_code_LT01_5_count 0.92417 0.06232 14.830 < 2e-16 ***
## category_code_LT01_10_count 0.10773 0.11470 0.939 0.34808
## category_code_LT01_15_count -0.03190 0.76211 -0.042 0.96663
## category_code_LT01_16_count 0.74191 1.18091 0.628 0.53013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.625454121185632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0190 -0.7591 0.0484 0.8734 3.4281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99760 0.08679 115.192 < 2e-16 ***
## category_code_LT01_3_count 0.28161 0.11574 2.433 0.015324 *
## category_code_LT01_4_count 0.76037 0.09380 8.106 4.19e-15 ***
## category_code_LT01_5_count 0.91833 0.06188 14.839 < 2e-16 ***
## category_code_LT01_11_count 0.40039 0.11729 3.414 0.000694 ***
## category_code_LT01_12_count -0.04334 0.21141 -0.205 0.837644
## category_code_LT01_13_count 0.07796 0.24248 0.322 0.747944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.625540321996465
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0191 -0.7518 0.0500 0.8722 3.4290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99936 0.08685 115.137 < 2e-16 ***
## category_code_LT01_3_count 0.28446 0.11587 2.455 0.014434 *
## category_code_LT01_4_count 0.75549 0.09484 7.966 1.15e-14 ***
## category_code_LT01_5_count 0.91591 0.06217 14.733 < 2e-16 ***
## category_code_LT01_11_count 0.40128 0.11715 3.425 0.000665 ***
## category_code_LT01_12_count -0.04950 0.21191 -0.234 0.815386
## category_code_LT01_14_count 0.15218 0.32711 0.465 0.641976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.625389327535865
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0193 -0.7613 0.0469 0.8759 3.4277
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99769 0.08680 115.183 < 2e-16 ***
## category_code_LT01_3_count 0.28367 0.11658 2.433 0.015319 *
## category_code_LT01_4_count 0.76382 0.09350 8.169 2.66e-15 ***
## category_code_LT01_5_count 0.91858 0.06189 14.842 < 2e-16 ***
## category_code_LT01_11_count 0.40296 0.11725 3.437 0.000639 ***
## category_code_LT01_12_count -0.04339 0.21153 -0.205 0.837570
## category_code_LT01_15_count -0.10217 0.75238 -0.136 0.892040
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.625680823210436
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0193 -0.7495 0.0445 0.8762 3.4279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99816 0.08677 115.233 < 2e-16 ***
## category_code_LT01_3_count 0.27299 0.11653 2.343 0.019551 *
## category_code_LT01_4_count 0.76434 0.09337 8.186 2.35e-15 ***
## category_code_LT01_5_count 0.91798 0.06186 14.839 < 2e-16 ***
## category_code_LT01_11_count 0.40148 0.11711 3.428 0.000659 ***
## category_code_LT01_12_count -0.04083 0.21134 -0.193 0.846899
## category_code_LT01_16_count 0.73813 1.16589 0.633 0.526964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.625576771731743
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0174 -0.7502 0.0528 0.8650 3.4314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99874 0.08682 115.164 < 2e-16 ***
## category_code_LT01_3_count 0.28380 0.11584 2.450 0.014639 *
## category_code_LT01_4_count 0.75255 0.09525 7.901 1.83e-14 ***
## category_code_LT01_5_count 0.91429 0.06199 14.750 < 2e-16 ***
## category_code_LT01_11_count 0.39215 0.11333 3.460 0.000587 ***
## category_code_LT01_13_count 0.07756 0.24242 0.320 0.749132
## category_code_LT01_14_count 0.14695 0.32624 0.450 0.652602
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6256
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.625430911006013
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0177 -0.7580 0.0505 0.8775 3.4299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99719 0.08678 115.205 < 2e-16 ***
## category_code_LT01_3_count 0.28277 0.11656 2.426 0.015628 *
## category_code_LT01_4_count 0.76061 0.09393 8.098 4.46e-15 ***
## category_code_LT01_5_count 0.91706 0.06167 14.870 < 2e-16 ***
## category_code_LT01_11_count 0.39460 0.11335 3.481 0.000543 ***
## category_code_LT01_13_count 0.07552 0.24301 0.311 0.756099
## category_code_LT01_15_count -0.08118 0.75360 -0.108 0.914259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.625741685400963
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.7441 0.0474 0.8775 3.4301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99765 0.08674 115.257 < 2e-16 ***
## category_code_LT01_3_count 0.27227 0.11651 2.337 0.019850 *
## category_code_LT01_4_count 0.76103 0.09377 8.116 3.9e-15 ***
## category_code_LT01_5_count 0.91643 0.06164 14.868 < 2e-16 ***
## category_code_LT01_11_count 0.39338 0.11323 3.474 0.000558 ***
## category_code_LT01_13_count 0.08302 0.24252 0.342 0.732270
## category_code_LT01_16_count 0.75537 1.16650 0.648 0.517575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.625512797315427
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0176 -0.7549 0.0524 0.8736 3.4310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99883 0.08683 115.155 < 2e-16 ***
## category_code_LT01_3_count 0.28587 0.11668 2.450 0.014638 *
## category_code_LT01_4_count 0.75597 0.09493 7.963 1.18e-14 ***
## category_code_LT01_5_count 0.91452 0.06200 14.751 < 2e-16 ***
## category_code_LT01_11_count 0.39469 0.11322 3.486 0.000534 ***
## category_code_LT01_14_count 0.14737 0.32631 0.452 0.651736
## category_code_LT01_15_count -0.10222 0.75191 -0.136 0.891922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.625826506144784
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0177 -0.7427 0.0505 0.8735 3.4312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99942 0.08680 115.207 < 2e-16 ***
## category_code_LT01_3_count 0.27503 0.11660 2.359 0.01873 *
## category_code_LT01_4_count 0.75611 0.09481 7.975 1.08e-14 ***
## category_code_LT01_5_count 0.91379 0.06196 14.747 < 2e-16 ***
## category_code_LT01_11_count 0.39344 0.11312 3.478 0.00055 ***
## category_code_LT01_14_count 0.15605 0.32645 0.478 0.63285
## category_code_LT01_16_count 0.76519 1.16670 0.656 0.51222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.625660116271137
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0181 -0.7580 0.0444 0.8789 3.4296
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99776 0.08675 115.245 < 2e-16 ***
## category_code_LT01_3_count 0.27409 0.11741 2.334 0.019977 *
## category_code_LT01_4_count 0.76448 0.09347 8.179 2.47e-15 ***
## category_code_LT01_5_count 0.91678 0.06165 14.871 < 2e-16 ***
## category_code_LT01_11_count 0.39596 0.11312 3.500 0.000507 ***
## category_code_LT01_15_count -0.07583 0.75241 -0.101 0.919769
## category_code_LT01_16_count 0.73556 1.16700 0.630 0.528791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616780684888298
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0137 -0.7578 0.0545 0.8765 3.4338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99317 0.08784 113.765 < 2e-16 ***
## category_code_LT01_3_count 0.38319 0.11347 3.377 0.000791 ***
## category_code_LT01_4_count 0.90418 0.08519 10.614 < 2e-16 ***
## category_code_LT01_5_count 0.91710 0.06290 14.579 < 2e-16 ***
## category_code_LT01_12_count 0.13549 0.20715 0.654 0.513377
## category_code_LT01_13_count 0.12039 0.24495 0.491 0.623307
## category_code_LT01_14_count 0.17389 0.33085 0.526 0.599406
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.61656611822731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7635 0.0473 0.8731 3.4320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99135 0.08780 113.801 < 2e-16 ***
## category_code_LT01_3_count 0.37986 0.11442 3.320 0.000968 ***
## category_code_LT01_4_count 0.91321 0.08367 10.915 < 2e-16 ***
## category_code_LT01_5_count 0.92043 0.06263 14.697 < 2e-16 ***
## category_code_LT01_12_count 0.14434 0.20661 0.699 0.485123
## category_code_LT01_13_count 0.12063 0.24553 0.491 0.623434
## category_code_LT01_15_count 0.02787 0.76213 0.037 0.970844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.616927767318628
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0140 -0.7581 0.0378 0.8738 3.4326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99176 0.08776 113.859 < 2e-16 ***
## category_code_LT01_3_count 0.37056 0.11425 3.243 0.00126 **
## category_code_LT01_4_count 0.91409 0.08332 10.971 < 2e-16 ***
## category_code_LT01_5_count 0.91948 0.06260 14.689 < 2e-16 ***
## category_code_LT01_12_count 0.14532 0.20646 0.704 0.48185
## category_code_LT01_13_count 0.12602 0.24506 0.514 0.60731
## category_code_LT01_16_count 0.80464 1.18013 0.682 0.49567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.6169
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616592176083334
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0144 -0.7693 0.0519 0.8712 3.4329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.993368 0.087863 113.739 < 2e-16 ***
## category_code_LT01_3_count 0.384251 0.114514 3.355 0.000854 ***
## category_code_LT01_4_count 0.909759 0.084679 10.744 < 2e-16 ***
## category_code_LT01_5_count 0.917795 0.062922 14.586 < 2e-16 ***
## category_code_LT01_12_count 0.138325 0.207192 0.668 0.504691
## category_code_LT01_14_count 0.173498 0.330991 0.524 0.600391
## category_code_LT01_15_count -0.003819 0.760658 -0.005 0.995996
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616960314821556
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7687 0.0412 0.8708 3.4335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99392 0.08782 113.797 < 2e-16 ***
## category_code_LT01_3_count 0.37444 0.11431 3.276 0.00113 **
## category_code_LT01_4_count 0.91011 0.08442 10.781 < 2e-16 ***
## category_code_LT01_5_count 0.91675 0.06289 14.577 < 2e-16 ***
## category_code_LT01_12_count 0.13917 0.20703 0.672 0.50175
## category_code_LT01_14_count 0.18320 0.33108 0.553 0.58027
## category_code_LT01_16_count 0.81090 1.18040 0.687 0.49243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616722363660118
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0148 -0.7624 0.0309 0.8804 3.4315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99198 0.08778 113.829 < 2e-16 ***
## category_code_LT01_3_count 0.37132 0.11535 3.219 0.00137 **
## category_code_LT01_4_count 0.91963 0.08284 11.102 < 2e-16 ***
## category_code_LT01_5_count 0.92028 0.06261 14.699 < 2e-16 ***
## category_code_LT01_12_count 0.14844 0.20650 0.719 0.47260
## category_code_LT01_15_count 0.02617 0.76114 0.034 0.97258
## category_code_LT01_16_count 0.78474 1.18085 0.665 0.50665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616446888682849
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0172 -0.7625 0.0481 0.8660 3.4290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.994218 0.087866 113.744 < 2e-16 ***
## category_code_LT01_3_count 0.389549 0.114106 3.414 0.000693 ***
## category_code_LT01_4_count 0.912142 0.084590 10.783 < 2e-16 ***
## category_code_LT01_5_count 0.920564 0.062729 14.675 < 2e-16 ***
## category_code_LT01_13_count 0.125066 0.245484 0.509 0.610654
## category_code_LT01_14_count 0.191076 0.329990 0.579 0.562831
## category_code_LT01_15_count 0.008414 0.762168 0.011 0.991196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616831176733969
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0170 -0.7716 0.0497 0.8638 3.4297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99477 0.08782 113.805 < 2e-16 ***
## category_code_LT01_3_count 0.37975 0.11391 3.334 0.000922 ***
## category_code_LT01_4_count 0.91236 0.08429 10.824 < 2e-16 ***
## category_code_LT01_5_count 0.91945 0.06270 14.665 < 2e-16 ***
## category_code_LT01_13_count 0.13109 0.24500 0.535 0.592857
## category_code_LT01_14_count 0.20120 0.33009 0.610 0.542449
## category_code_LT01_16_count 0.82910 1.18135 0.702 0.483122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616543379473617
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.7753 0.0340 0.8641 3.4271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99271 0.08779 113.823 < 2e-16 ***
## category_code_LT01_3_count 0.37657 0.11499 3.275 0.00113 **
## category_code_LT01_4_count 0.92339 0.08251 11.191 < 2e-16 ***
## category_code_LT01_5_count 0.92363 0.06237 14.808 < 2e-16 ***
## category_code_LT01_13_count 0.13165 0.24564 0.536 0.59222
## category_code_LT01_15_count 0.03997 0.76279 0.052 0.95823
## category_code_LT01_16_count 0.80070 1.18196 0.677 0.49845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.616607813083
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0178 -0.7645 0.0337 0.8640 3.4284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.994999 0.087851 113.773 < 2e-16 ***
## category_code_LT01_3_count 0.381131 0.114989 3.314 0.000986 ***
## category_code_LT01_4_count 0.918537 0.083710 10.973 < 2e-16 ***
## category_code_LT01_5_count 0.920340 0.062715 14.675 < 2e-16 ***
## category_code_LT01_14_count 0.200819 0.330222 0.608 0.543380
## category_code_LT01_15_count 0.005698 0.761110 0.007 0.994030
## category_code_LT01_16_count 0.806703 1.182044 0.682 0.495267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.600674984549784
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0753 -0.8209 0.0974 0.9199 3.7696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.03134 0.08953 112.039 < 2e-16 ***
## category_code_LT01_3_count 0.53428 0.11256 4.747 2.72e-06 ***
## category_code_LT01_5_count 0.95081 0.06425 14.798 < 2e-16 ***
## category_code_LT01_6_count 0.62488 0.15327 4.077 5.32e-05 ***
## category_code_LT01_7_count 0.62192 0.15998 3.887 0.000115 ***
## category_code_LT01_8_count -0.17247 0.28231 -0.611 0.541528
## category_code_LT01_11_count 0.56806 0.11366 4.998 8.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6055, Adjusted R-squared: 0.6007
## F-statistic: 125.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.602376193584573
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0611 -0.8329 0.0887 0.9372 3.7754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.02558 0.08935 112.202 < 2e-16 ***
## category_code_LT01_3_count 0.50395 0.11371 4.432 1.15e-05 ***
## category_code_LT01_5_count 0.93871 0.06357 14.766 < 2e-16 ***
## category_code_LT01_6_count 0.60648 0.15305 3.963 8.51e-05 ***
## category_code_LT01_7_count 0.59227 0.16033 3.694 0.000245 ***
## category_code_LT01_9_count 0.36778 0.23375 1.573 0.116273
## category_code_LT01_11_count 0.56240 0.11345 4.957 9.86e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.42 on 491 degrees of freedom
## Multiple R-squared: 0.6072, Adjusted R-squared: 0.6024
## F-statistic: 126.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.600416907190466
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0644 -0.8302 0.0638 0.9223 3.7768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.02417 0.09273 108.106 < 2e-16 ***
## category_code_LT01_3_count 0.52764 0.11427 4.617 4.97e-06 ***
## category_code_LT01_5_count 0.94541 0.06361 14.863 < 2e-16 ***
## category_code_LT01_6_count 0.61500 0.15500 3.968 8.34e-05 ***
## category_code_LT01_7_count 0.61476 0.16041 3.832 0.000143 ***
## category_code_LT01_10_count 0.02804 0.11863 0.236 0.813236
## category_code_LT01_11_count 0.57190 0.11364 5.032 6.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 491 degrees of freedom
## Multiple R-squared: 0.6052, Adjusted R-squared: 0.6004
## F-statistic: 125.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.600403459931646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0709 -0.8263 0.0719 0.9251 3.7708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.03015 0.08954 112.014 < 2e-16 ***
## category_code_LT01_3_count 0.53258 0.11256 4.732 2.92e-06 ***
## category_code_LT01_5_count 0.94619 0.06382 14.826 < 2e-16 ***
## category_code_LT01_6_count 0.62320 0.15371 4.054 5.84e-05 ***
## category_code_LT01_7_count 0.61611 0.16012 3.848 0.000135 ***
## category_code_LT01_11_count 0.57749 0.11807 4.891 1.36e-06 ***
## category_code_LT01_12_count -0.04352 0.21938 -0.198 0.842838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 491 degrees of freedom
## Multiple R-squared: 0.6052, Adjusted R-squared: 0.6004
## F-statistic: 125.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.600639963954152
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0685 -0.8413 0.0720 0.9337 3.7717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.02922 0.08951 112.041 < 2e-16 ***
## category_code_LT01_3_count 0.53007 0.11258 4.708 3.26e-06 ***
## category_code_LT01_5_count 0.94407 0.06361 14.842 < 2e-16 ***
## category_code_LT01_6_count 0.62076 0.15312 4.054 5.85e-05 ***
## category_code_LT01_7_count 0.60629 0.16110 3.763 0.000188 ***
## category_code_LT01_11_count 0.56706 0.11378 4.984 8.65e-07 ***
## category_code_LT01_13_count 0.14433 0.25119 0.575 0.565835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6055, Adjusted R-squared: 0.6006
## F-statistic: 125.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.602560772601835
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0628 -0.8285 0.0918 0.9118 3.7686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.03237 0.08930 112.340 < 2e-16 ***
## category_code_LT01_3_count 0.52846 0.11227 4.707 3.27e-06 ***
## category_code_LT01_5_count 0.93128 0.06398 14.555 < 2e-16 ***
## category_code_LT01_6_count 0.63304 0.15293 4.139 4.10e-05 ***
## category_code_LT01_7_count 0.59163 0.16026 3.692 0.000248 ***
## category_code_LT01_11_count 0.55072 0.11396 4.832 1.81e-06 ***
## category_code_LT01_14_count 0.54750 0.33291 1.645 0.100691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.42 on 491 degrees of freedom
## Multiple R-squared: 0.6074, Adjusted R-squared: 0.6026
## F-statistic: 126.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.600402860921286
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0701 -0.8257 0.0693 0.9324 3.7710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.02996 0.08953 112.024 < 2e-16 ***
## category_code_LT01_3_count 0.52921 0.11366 4.656 4.15e-06 ***
## category_code_LT01_5_count 0.94541 0.06361 14.862 < 2e-16 ***
## category_code_LT01_6_count 0.61927 0.15332 4.039 6.22e-05 ***
## category_code_LT01_7_count 0.61883 0.15998 3.868 0.000124 ***
## category_code_LT01_11_count 0.56980 0.11379 5.008 7.70e-07 ***
## category_code_LT01_15_count 0.15267 0.77690 0.197 0.844296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.424 on 491 degrees of freedom
## Multiple R-squared: 0.6052, Adjusted R-squared: 0.6004
## F-statistic: 125.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_3_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.600935307806646
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0690 -0.8249 0.0835 0.9207 3.7710
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.03000 0.08947 112.101 < 2e-16 ***
## category_code_LT01_3_count 0.51977 0.11348 4.580 5.89e-06 ***
## category_code_LT01_5_count 0.94366 0.06358 14.842 < 2e-16 ***
## category_code_LT01_6_count 0.63108 0.15357 4.109 4.65e-05 ***
## category_code_LT01_7_count 0.61890 0.15980 3.873 0.000122 ***
## category_code_LT01_11_count 0.56873 0.11355 5.009 7.66e-07 ***
## category_code_LT01_16_count 1.00584 1.20759 0.833 0.405288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.423 on 491 degrees of freedom
## Multiple R-squared: 0.6058, Adjusted R-squared: 0.6009
## F-statistic: 125.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count 0.627422103123569
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9983 -0.7664 0.0037 0.9177 4.0567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98521 0.08667 115.208 < 2e-16 ***
## category_code_LT01_4_count 0.84918 0.08012 10.599 < 2e-16 ***
## category_code_LT01_5_count 0.90628 0.06250 14.501 < 2e-16 ***
## category_code_LT01_6_count 0.48382 0.14909 3.245 0.00125 **
## category_code_LT01_7_count 0.48894 0.15330 3.190 0.00152 **
## category_code_LT01_8_count -0.20855 0.27252 -0.765 0.44449
## category_code_LT01_9_count 0.41805 0.22395 1.867 0.06254 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count 0.6252151677489
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9910 -0.7604 0.0445 0.9208 3.9752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97251 0.09012 110.652 < 2e-16 ***
## category_code_LT01_4_count 0.86637 0.07972 10.867 < 2e-16 ***
## category_code_LT01_5_count 0.91411 0.06255 14.613 < 2e-16 ***
## category_code_LT01_6_count 0.48418 0.15131 3.200 0.001464 **
## category_code_LT01_7_count 0.51003 0.15340 3.325 0.000951 ***
## category_code_LT01_8_count -0.19729 0.27325 -0.722 0.470642
## category_code_LT01_10_count 0.08574 0.11328 0.757 0.449481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6297, Adjusted R-squared: 0.6252
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count 0.631238679879627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0090 -0.7553 0.0271 0.9617 3.8156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99404 0.08619 115.960 < 2e-16 ***
## category_code_LT01_4_count 0.73608 0.09123 8.068 5.52e-15 ***
## category_code_LT01_5_count 0.90908 0.06207 14.646 < 2e-16 ***
## category_code_LT01_6_count 0.41772 0.15082 2.770 0.00582 **
## category_code_LT01_7_count 0.39985 0.15704 2.546 0.01120 *
## category_code_LT01_8_count -0.16474 0.27121 -0.607 0.54385
## category_code_LT01_11_count 0.33513 0.11426 2.933 0.00351 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6357, Adjusted R-squared: 0.6312
## F-statistic: 142.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count 0.625004263832025
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0066 -0.7653 0.0347 0.9252 4.0517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99018 0.08691 114.952 < 2e-16 ***
## category_code_LT01_4_count 0.86325 0.08065 10.704 < 2e-16 ***
## category_code_LT01_5_count 0.91110 0.06277 14.515 < 2e-16 ***
## category_code_LT01_6_count 0.49290 0.15045 3.276 0.00113 **
## category_code_LT01_7_count 0.51867 0.15291 3.392 0.00075 ***
## category_code_LT01_8_count -0.19907 0.27344 -0.728 0.46694
## category_code_LT01_12_count 0.11178 0.20531 0.544 0.58639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count 0.624800004387154
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0086 -0.7680 0.0169 0.9169 4.0514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99053 0.08693 114.926 < 2e-16 ***
## category_code_LT01_4_count 0.86889 0.08009 10.849 < 2e-16 ***
## category_code_LT01_5_count 0.91349 0.06262 14.589 < 2e-16 ***
## category_code_LT01_6_count 0.50364 0.14925 3.374 0.000798 ***
## category_code_LT01_7_count 0.51670 0.15408 3.354 0.000860 ***
## category_code_LT01_8_count -0.19149 0.27390 -0.699 0.484797
## category_code_LT01_13_count 0.04159 0.24446 0.170 0.864977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count 0.624980676567906
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0082 -0.7625 0.0293 0.9222 4.0496
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99225 0.08696 114.904 < 2e-16 ***
## category_code_LT01_4_count 0.86149 0.08144 10.579 < 2e-16 ***
## category_code_LT01_5_count 0.91038 0.06293 14.468 < 2e-16 ***
## category_code_LT01_6_count 0.51029 0.14981 3.406 0.000713 ***
## category_code_LT01_7_count 0.51422 0.15329 3.355 0.000857 ***
## category_code_LT01_8_count -0.19661 0.27334 -0.719 0.472321
## category_code_LT01_14_count 0.16908 0.32814 0.515 0.606594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count 0.624873323167473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0093 -0.7689 0.0217 0.9075 4.0511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99073 0.08692 114.944 < 2e-16 ***
## category_code_LT01_4_count 0.86636 0.08040 10.775 < 2e-16 ***
## category_code_LT01_5_count 0.91423 0.06259 14.606 < 2e-16 ***
## category_code_LT01_6_count 0.50051 0.14946 3.349 0.000874 ***
## category_code_LT01_7_count 0.52132 0.15297 3.408 0.000708 ***
## category_code_LT01_8_count -0.19534 0.27336 -0.715 0.475213
## category_code_LT01_15_count 0.26399 0.74692 0.353 0.723914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6249
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_16_count 0.626126991646204
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0068 -0.7655 0.0237 0.9243 4.0517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99021 0.08677 115.130 < 2e-16 ***
## category_code_LT01_4_count 0.86138 0.07974 10.803 < 2e-16 ***
## category_code_LT01_5_count 0.91137 0.06250 14.581 < 2e-16 ***
## category_code_LT01_6_count 0.51594 0.14928 3.456 0.000595 ***
## category_code_LT01_7_count 0.51925 0.15266 3.401 0.000725 ***
## category_code_LT01_8_count -0.21347 0.27327 -0.781 0.435074
## category_code_LT01_16_count 1.54354 1.15962 1.331 0.183783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.6261
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count 0.627203962352045
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9791 -0.7577 0.0021 0.9183 4.0033
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97042 0.08986 110.954 < 2e-16 ***
## category_code_LT01_4_count 0.84753 0.08025 10.562 < 2e-16 ***
## category_code_LT01_5_count 0.89991 0.06189 14.541 < 2e-16 ***
## category_code_LT01_6_count 0.46634 0.15102 3.088 0.00213 **
## category_code_LT01_7_count 0.47949 0.15369 3.120 0.00192 **
## category_code_LT01_9_count 0.39952 0.22534 1.773 0.07685 .
## category_code_LT01_10_count 0.06205 0.11368 0.546 0.58541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6317, Adjusted R-squared: 0.6272
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count 0.633048565045531
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9939 -0.7662 0.0280 0.9296 3.8289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98749 0.08599 116.142 < 2e-16 ***
## category_code_LT01_4_count 0.72140 0.09138 7.895 1.92e-14 ***
## category_code_LT01_5_count 0.89664 0.06141 14.602 < 2e-16 ***
## category_code_LT01_6_count 0.39904 0.15055 2.651 0.00830 **
## category_code_LT01_7_count 0.37269 0.15715 2.372 0.01810 *
## category_code_LT01_9_count 0.37204 0.22263 1.671 0.09534 .
## category_code_LT01_11_count 0.32532 0.11414 2.850 0.00455 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.364 on 491 degrees of freedom
## Multiple R-squared: 0.6375, Adjusted R-squared: 0.633
## F-statistic: 143.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count 0.627166506276007
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9895 -0.7680 0.0045 0.9246 4.0589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98294 0.08667 115.179 < 2e-16 ***
## category_code_LT01_4_count 0.84330 0.08119 10.387 < 2e-16 ***
## category_code_LT01_5_count 0.89691 0.06210 14.442 < 2e-16 ***
## category_code_LT01_6_count 0.47004 0.15029 3.128 0.00187 **
## category_code_LT01_7_count 0.48455 0.15330 3.161 0.00167 **
## category_code_LT01_9_count 0.41178 0.22396 1.839 0.06657 .
## category_code_LT01_12_count 0.10203 0.20463 0.499 0.61828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6317, Adjusted R-squared: 0.6272
## F-statistic: 140.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count 0.627068074711384
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9911 -0.7554 0.0048 0.9199 4.0588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98312 0.08668 115.168 < 2e-16 ***
## category_code_LT01_4_count 0.84639 0.08075 10.482 < 2e-16 ***
## category_code_LT01_5_count 0.89894 0.06192 14.519 < 2e-16 ***
## category_code_LT01_6_count 0.47998 0.14907 3.220 0.00137 **
## category_code_LT01_7_count 0.47888 0.15456 3.098 0.00206 **
## category_code_LT01_9_count 0.41871 0.22452 1.865 0.06279 .
## category_code_LT01_13_count 0.08410 0.24386 0.345 0.73033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6316, Adjusted R-squared: 0.6271
## F-statistic: 140.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count 0.627087283857417
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9913 -0.7546 0.0052 0.9174 4.0572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98463 0.08674 115.111 < 2e-16 ***
## category_code_LT01_4_count 0.84348 0.08184 10.306 < 2e-16 ***
## category_code_LT01_5_count 0.89703 0.06223 14.414 < 2e-16 ***
## category_code_LT01_6_count 0.48496 0.14972 3.239 0.00128 **
## category_code_LT01_7_count 0.48186 0.15363 3.137 0.00181 **
## category_code_LT01_9_count 0.40743 0.22449 1.815 0.07015 .
## category_code_LT01_14_count 0.12455 0.32794 0.380 0.70427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6316, Adjusted R-squared: 0.6271
## F-statistic: 140.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count 0.627086002070507
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9921 -0.7569 -0.0017 0.9131 4.0584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98345 0.08668 115.179 < 2e-16 ***
## category_code_LT01_4_count 0.84537 0.08099 10.438 < 2e-16 ***
## category_code_LT01_5_count 0.89988 0.06190 14.537 < 2e-16 ***
## category_code_LT01_6_count 0.47648 0.14930 3.191 0.00151 **
## category_code_LT01_7_count 0.48703 0.15335 3.176 0.00159 **
## category_code_LT01_9_count 0.41465 0.22399 1.851 0.06474 .
## category_code_LT01_15_count 0.28120 0.74477 0.378 0.70592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6316, Adjusted R-squared: 0.6271
## F-statistic: 140.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_16_count 0.628071401101371
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7559 0.0042 0.9149 4.0589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98303 0.08656 115.326 < 2e-16 ***
## category_code_LT01_4_count 0.84236 0.08029 10.492 < 2e-16 ***
## category_code_LT01_5_count 0.89699 0.06185 14.503 < 2e-16 ***
## category_code_LT01_6_count 0.49123 0.14918 3.293 0.00106 **
## category_code_LT01_7_count 0.48579 0.15310 3.173 0.00160 **
## category_code_LT01_9_count 0.39940 0.22396 1.783 0.07515 .
## category_code_LT01_16_count 1.38968 1.15654 1.202 0.23010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6326, Adjusted R-squared: 0.6281
## F-statistic: 140.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count 0.631329724218407
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9872 -0.7570 0.0178 0.9542 3.7465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97589 0.08938 111.616 < 2e-16 ***
## category_code_LT01_4_count 0.73217 0.09132 8.017 7.96e-15 ***
## category_code_LT01_5_count 0.90382 0.06141 14.718 < 2e-16 ***
## category_code_LT01_6_count 0.39625 0.15267 2.595 0.00973 **
## category_code_LT01_7_count 0.38745 0.15738 2.462 0.01416 *
## category_code_LT01_10_count 0.07868 0.11236 0.700 0.48412
## category_code_LT01_11_count 0.33628 0.11419 2.945 0.00338 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6358, Adjusted R-squared: 0.6313
## F-statistic: 142.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count 0.625008272658611
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9830 -0.7515 0.0310 0.9160 3.9799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97084 0.09012 110.634 < 2e-16 ***
## category_code_LT01_4_count 0.86024 0.08080 10.647 < 2e-16 ***
## category_code_LT01_5_count 0.90497 0.06214 14.563 < 2e-16 ***
## category_code_LT01_6_count 0.47091 0.15242 3.090 0.00212 **
## category_code_LT01_7_count 0.50569 0.15339 3.297 0.00105 **
## category_code_LT01_10_count 0.08293 0.11335 0.732 0.46474
## category_code_LT01_12_count 0.10267 0.20530 0.500 0.61724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count 0.624846625352967
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9848 -0.7542 0.0221 0.9105 3.9786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97097 0.09014 110.612 < 2e-16 ***
## category_code_LT01_4_count 0.86500 0.08025 10.779 < 2e-16 ***
## category_code_LT01_5_count 0.90735 0.06195 14.648 < 2e-16 ***
## category_code_LT01_6_count 0.48073 0.15131 3.177 0.00158 **
## category_code_LT01_7_count 0.50315 0.15447 3.257 0.00120 **
## category_code_LT01_10_count 0.08406 0.11336 0.742 0.45872
## category_code_LT01_13_count 0.04783 0.24405 0.196 0.84469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6248
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count 0.624906651175078
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7491 0.0327 0.9121 3.9850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97398 0.09056 110.131 < 2e-16 ***
## category_code_LT01_4_count 0.86108 0.08145 10.572 < 2e-16 ***
## category_code_LT01_5_count 0.90519 0.06233 14.524 < 2e-16 ***
## category_code_LT01_6_count 0.48699 0.15254 3.192 0.00150 **
## category_code_LT01_7_count 0.50386 0.15362 3.280 0.00111 **
## category_code_LT01_10_count 0.07547 0.11641 0.648 0.51707
## category_code_LT01_14_count 0.11529 0.33707 0.342 0.73246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6249
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count 0.62488093453465
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9858 -0.7567 0.0241 0.9087 3.9802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97157 0.09016 110.594 < 2e-16 ***
## category_code_LT01_4_count 0.86354 0.08052 10.724 < 2e-16 ***
## category_code_LT01_5_count 0.90792 0.06194 14.659 < 2e-16 ***
## category_code_LT01_6_count 0.47847 0.15142 3.160 0.001675 **
## category_code_LT01_7_count 0.50815 0.15348 3.311 0.000999 ***
## category_code_LT01_10_count 0.08201 0.11367 0.722 0.470936
## category_code_LT01_15_count 0.21626 0.74917 0.289 0.772960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6249
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_16_count 0.626003210008191
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9842 -0.7497 0.0373 0.9075 3.9863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97225 0.09001 110.790 < 2e-16 ***
## category_code_LT01_4_count 0.85870 0.07990 10.747 < 2e-16 ***
## category_code_LT01_5_count 0.90470 0.06188 14.620 < 2e-16 ***
## category_code_LT01_6_count 0.49362 0.15144 3.260 0.00119 **
## category_code_LT01_7_count 0.50679 0.15317 3.309 0.00101 **
## category_code_LT01_10_count 0.07584 0.11337 0.669 0.50382
## category_code_LT01_16_count 1.44796 1.16043 1.248 0.21271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count 0.631009234682745
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0050 -0.7470 0.0196 0.9587 3.8095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99291 0.08619 115.942 < 2e-16 ***
## category_code_LT01_4_count 0.73560 0.09126 8.060 5.84e-15 ***
## category_code_LT01_5_count 0.90489 0.06164 14.680 < 2e-16 ***
## category_code_LT01_6_count 0.41666 0.15121 2.756 0.00608 **
## category_code_LT01_7_count 0.39390 0.15719 2.506 0.01254 *
## category_code_LT01_11_count 0.34546 0.11829 2.920 0.00366 **
## category_code_LT01_12_count -0.05309 0.21082 -0.252 0.80126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6355, Adjusted R-squared: 0.631
## F-statistic: 142.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count 0.630972073518676
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0036 -0.7567 0.0294 0.9673 3.8156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99250 0.08619 115.942 < 2e-16 ***
## category_code_LT01_4_count 0.73443 0.09156 8.022 7.72e-15 ***
## category_code_LT01_5_count 0.90347 0.06145 14.703 < 2e-16 ***
## category_code_LT01_6_count 0.41390 0.15074 2.746 0.00626 **
## category_code_LT01_7_count 0.39403 0.15788 2.496 0.01290 *
## category_code_LT01_11_count 0.33727 0.11428 2.951 0.00332 **
## category_code_LT01_13_count 0.02862 0.24211 0.118 0.90594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.631
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count 0.631099414558602
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0031 -0.7546 0.0411 0.9263 3.8148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99387 0.08622 115.910 < 2e-16 ***
## category_code_LT01_4_count 0.72848 0.09262 7.865 2.37e-14 ***
## category_code_LT01_5_count 0.90074 0.06179 14.577 < 2e-16 ***
## category_code_LT01_6_count 0.41956 0.15134 2.772 0.00578 **
## category_code_LT01_7_count 0.39180 0.15726 2.491 0.01305 *
## category_code_LT01_11_count 0.33639 0.11424 2.944 0.00339 **
## category_code_LT01_14_count 0.13943 0.32552 0.428 0.66860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6356, Adjusted R-squared: 0.6311
## F-statistic: 142.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count 0.630979363889037
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0039 -0.7435 0.0299 0.9685 3.8160
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99260 0.08618 115.947 < 2e-16 ***
## category_code_LT01_4_count 0.73402 0.09164 8.010 8.4e-15 ***
## category_code_LT01_5_count 0.90380 0.06145 14.709 < 2e-16 ***
## category_code_LT01_6_count 0.41262 0.15085 2.735 0.00646 **
## category_code_LT01_7_count 0.39706 0.15711 2.527 0.01181 *
## category_code_LT01_11_count 0.33654 0.11447 2.940 0.00344 **
## category_code_LT01_15_count 0.11424 0.74240 0.154 0.87777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 491 degrees of freedom
## Multiple R-squared: 0.6354, Adjusted R-squared: 0.631
## F-statistic: 142.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_16_count 0.631939784492846
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0014 -0.7516 0.0412 0.9601 3.8207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99198 0.08607 116.089 < 2e-16 ***
## category_code_LT01_4_count 0.73049 0.09124 8.007 8.6e-15 ***
## category_code_LT01_5_count 0.90108 0.06140 14.676 < 2e-16 ***
## category_code_LT01_6_count 0.42572 0.15090 2.821 0.00498 **
## category_code_LT01_7_count 0.39774 0.15677 2.537 0.01149 *
## category_code_LT01_11_count 0.33060 0.11424 2.894 0.00397 **
## category_code_LT01_16_count 1.31449 1.15069 1.142 0.25387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.366 on 491 degrees of freedom
## Multiple R-squared: 0.6364, Adjusted R-squared: 0.6319
## F-statistic: 143.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count 0.62462951700135
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0001 -0.7467 0.0099 0.9279 4.0536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98829 0.08692 114.916 < 2e-16 ***
## category_code_LT01_4_count 0.86217 0.08114 10.626 < 2e-16 ***
## category_code_LT01_5_count 0.90443 0.06218 14.545 < 2e-16 ***
## category_code_LT01_6_count 0.48960 0.15046 3.254 0.001216 **
## category_code_LT01_7_count 0.51159 0.15401 3.322 0.000961 ***
## category_code_LT01_12_count 0.10585 0.20540 0.515 0.606553
## category_code_LT01_13_count 0.04842 0.24416 0.198 0.842887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count 0.624761097561277
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9998 -0.7346 0.0204 0.9242 4.0520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98985 0.08696 114.880 < 2e-16 ***
## category_code_LT01_4_count 0.85640 0.08229 10.407 < 2e-16 ***
## category_code_LT01_5_count 0.90173 0.06249 14.431 < 2e-16 ***
## category_code_LT01_6_count 0.49606 0.15116 3.282 0.001105 **
## category_code_LT01_7_count 0.51020 0.15327 3.329 0.000938 ***
## category_code_LT01_12_count 0.09876 0.20606 0.479 0.631946
## category_code_LT01_14_count 0.15152 0.32945 0.460 0.645788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count 0.624696577505383
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0006 -0.7431 0.0023 0.9179 4.0534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98846 0.08691 114.930 < 2e-16 ***
## category_code_LT01_4_count 0.85969 0.08153 10.545 < 2e-16 ***
## category_code_LT01_5_count 0.90501 0.06217 14.556 < 2e-16 ***
## category_code_LT01_6_count 0.48607 0.15067 3.226 0.001339 **
## category_code_LT01_7_count 0.51663 0.15295 3.378 0.000789 ***
## category_code_LT01_12_count 0.10848 0.20533 0.528 0.597504
## category_code_LT01_15_count 0.26635 0.74720 0.356 0.721646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6247
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_16_count 0.625860471866177
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9977 -0.7365 0.0322 0.9263 4.0541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98782 0.08677 115.100 < 2e-16 ***
## category_code_LT01_4_count 0.85534 0.08083 10.583 < 2e-16 ***
## category_code_LT01_5_count 0.90175 0.06211 14.518 < 2e-16 ***
## category_code_LT01_6_count 0.50110 0.15048 3.330 0.000934 ***
## category_code_LT01_7_count 0.51431 0.15266 3.369 0.000814 ***
## category_code_LT01_12_count 0.10453 0.20498 0.510 0.610321
## category_code_LT01_16_count 1.49030 1.15848 1.286 0.198901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count 0.624622191063939
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0016 -0.7403 0.0295 0.9189 4.0516
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99032 0.08697 114.873 < 2e-16 ***
## category_code_LT01_4_count 0.85999 0.08198 10.490 < 2e-16 ***
## category_code_LT01_5_count 0.90367 0.06234 14.497 < 2e-16 ***
## category_code_LT01_6_count 0.50640 0.14977 3.381 0.000779 ***
## category_code_LT01_7_count 0.50683 0.15441 3.282 0.001103 **
## category_code_LT01_13_count 0.05344 0.24407 0.219 0.826776
## category_code_LT01_14_count 0.16609 0.32827 0.506 0.613123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count 0.624525174108632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0027 -0.7590 0.0164 0.9113 4.0530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98883 0.08693 114.913 < 2e-16 ***
## category_code_LT01_4_count 0.86449 0.08102 10.670 < 2e-16 ***
## category_code_LT01_5_count 0.90749 0.06198 14.642 < 2e-16 ***
## category_code_LT01_6_count 0.49674 0.14942 3.324 0.000952 ***
## category_code_LT01_7_count 0.51359 0.15405 3.334 0.000921 ***
## category_code_LT01_13_count 0.05728 0.24451 0.234 0.814890
## category_code_LT01_15_count 0.26889 0.74852 0.359 0.719577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6291, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_16_count 0.62571331691632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9997 -0.7519 0.0265 0.9203 4.0537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98815 0.08679 115.086 < 2e-16 ***
## category_code_LT01_4_count 0.85962 0.08031 10.704 < 2e-16 ***
## category_code_LT01_5_count 0.90406 0.06192 14.601 < 2e-16 ***
## category_code_LT01_6_count 0.51159 0.14925 3.428 0.000660 ***
## category_code_LT01_7_count 0.51076 0.15378 3.321 0.000963 ***
## category_code_LT01_13_count 0.06307 0.24384 0.259 0.796019
## category_code_LT01_16_count 1.50631 1.15935 1.299 0.194462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6302, Adjusted R-squared: 0.6257
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count 0.624672324315354
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0022 -0.7513 0.0196 0.9114 4.0514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99049 0.08696 114.885 < 2e-16 ***
## category_code_LT01_4_count 0.85824 0.08223 10.438 < 2e-16 ***
## category_code_LT01_5_count 0.90439 0.06233 14.510 < 2e-16 ***
## category_code_LT01_6_count 0.50312 0.14999 3.354 0.000857 ***
## category_code_LT01_7_count 0.51230 0.15334 3.341 0.000899 ***
## category_code_LT01_14_count 0.16328 0.32828 0.497 0.619139
## category_code_LT01_15_count 0.25177 0.74720 0.337 0.736303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6247
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_16_count 0.625914370730453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9991 -0.7349 0.0431 0.9160 4.0518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99005 0.08682 115.070 < 2e-16 ***
## category_code_LT01_4_count 0.85182 0.08169 10.427 < 2e-16 ***
## category_code_LT01_5_count 0.90044 0.06227 14.460 < 2e-16 ***
## category_code_LT01_6_count 0.51897 0.14982 3.464 0.000579 ***
## category_code_LT01_7_count 0.50913 0.15304 3.327 0.000944 ***
## category_code_LT01_14_count 0.18875 0.32817 0.575 0.565439
## category_code_LT01_16_count 1.53202 1.16004 1.321 0.187231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6304, Adjusted R-squared: 0.6259
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_7_count+category_code_LT01_15_count+category_code_LT01_16_count 0.625770699246897
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0004 -0.7577 0.0292 0.9138 4.0535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98838 0.08678 115.099 < 2e-16 ***
## category_code_LT01_4_count 0.85762 0.08061 10.639 < 2e-16 ***
## category_code_LT01_5_count 0.90481 0.06191 14.616 < 2e-16 ***
## category_code_LT01_6_count 0.50804 0.14944 3.400 0.000730 ***
## category_code_LT01_7_count 0.51703 0.15271 3.386 0.000767 ***
## category_code_LT01_15_count 0.28138 0.74619 0.377 0.706273
## category_code_LT01_16_count 1.50621 1.15889 1.300 0.194314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6303, Adjusted R-squared: 0.6258
## F-statistic: 139.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.620171101905003
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9863 -0.7731 0.0472 0.9518 3.9775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96655 0.09071 109.870 < 2e-16 ***
## category_code_LT01_4_count 0.94387 0.07476 12.625 < 2e-16 ***
## category_code_LT01_5_count 0.91596 0.06304 14.530 < 2e-16 ***
## category_code_LT01_6_count 0.46979 0.15253 3.080 0.00219 **
## category_code_LT01_8_count -0.18685 0.27508 -0.679 0.49729
## category_code_LT01_9_count 0.47424 0.22641 2.095 0.03672 *
## category_code_LT01_10_count 0.08907 0.11445 0.778 0.43682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.629089406896759
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0042 -0.7621 0.0410 0.9446 3.7806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98977 0.08649 115.506 < 2e-16 ***
## category_code_LT01_4_count 0.76661 0.08995 8.522 < 2e-16 ***
## category_code_LT01_5_count 0.90827 0.06233 14.573 < 2e-16 ***
## category_code_LT01_6_count 0.38973 0.15139 2.574 0.010335 *
## category_code_LT01_8_count -0.15456 0.27194 -0.568 0.570054
## category_code_LT01_9_count 0.42327 0.22307 1.897 0.058357 .
## category_code_LT01_11_count 0.39163 0.11110 3.525 0.000463 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6336, Adjusted R-squared: 0.6291
## F-statistic: 141.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.619945107958319
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0022 -0.7775 0.0407 0.9308 4.0572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98467 0.08754 114.057 < 2e-16 ***
## category_code_LT01_4_count 0.94130 0.07573 12.430 < 2e-16 ***
## category_code_LT01_5_count 0.91266 0.06325 14.429 < 2e-16 ***
## category_code_LT01_6_count 0.47812 0.15181 3.149 0.00174 **
## category_code_LT01_8_count -0.18892 0.27527 -0.686 0.49285
## category_code_LT01_9_count 0.49355 0.22488 2.195 0.02865 *
## category_code_LT01_12_count 0.11569 0.20669 0.560 0.57593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.6199
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.620082539655644
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0027 -0.7740 0.0289 0.9102 4.0574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98449 0.08753 114.075 < 2e-16 ***
## category_code_LT01_4_count 0.93920 0.07577 12.396 < 2e-16 ***
## category_code_LT01_5_count 0.91369 0.06309 14.481 < 2e-16 ***
## category_code_LT01_6_count 0.48924 0.15055 3.250 0.00123 **
## category_code_LT01_8_count -0.17329 0.27551 -0.629 0.52967
## category_code_LT01_9_count 0.50394 0.22515 2.238 0.02565 *
## category_code_LT01_13_count 0.17135 0.24453 0.701 0.48381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6247, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619971222205135
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0038 -0.7767 0.0185 0.9273 4.0548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98710 0.08760 114.012 < 2e-16 ***
## category_code_LT01_4_count 0.93786 0.07682 12.209 < 2e-16 ***
## category_code_LT01_5_count 0.91157 0.06340 14.378 < 2e-16 ***
## category_code_LT01_6_count 0.49722 0.15122 3.288 0.00108 **
## category_code_LT01_8_count -0.18661 0.27516 -0.678 0.49797
## category_code_LT01_9_count 0.48531 0.22549 2.152 0.03186 *
## category_code_LT01_14_count 0.19464 0.33040 0.589 0.55606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.619774578422417
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0049 -0.7748 0.0325 0.9361 4.0567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98519 0.08756 114.043 < 2e-16 ***
## category_code_LT01_4_count 0.94553 0.07533 12.552 < 2e-16 ***
## category_code_LT01_5_count 0.91583 0.06308 14.518 < 2e-16 ***
## category_code_LT01_6_count 0.48638 0.15084 3.224 0.00135 **
## category_code_LT01_8_count -0.18487 0.27521 -0.672 0.50207
## category_code_LT01_9_count 0.49664 0.22495 2.208 0.02772 *
## category_code_LT01_15_count 0.22920 0.75186 0.305 0.76061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6198
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.620858192667815
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0030 -0.7783 0.0322 0.9333 4.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98491 0.08743 114.202 < 2e-16 ***
## category_code_LT01_4_count 0.94129 0.07468 12.604 < 2e-16 ***
## category_code_LT01_5_count 0.91345 0.06300 14.499 < 2e-16 ***
## category_code_LT01_6_count 0.50121 0.15073 3.325 0.000949 ***
## category_code_LT01_8_count -0.20126 0.27517 -0.731 0.464885
## category_code_LT01_9_count 0.48150 0.22487 2.141 0.032745 *
## category_code_LT01_16_count 1.43034 1.16923 1.223 0.221796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6209
## F-statistic: 136.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.626983302729911
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9941 -0.7529 -0.0114 0.9305 3.6745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97403 0.08992 110.925 < 2e-16 ***
## category_code_LT01_4_count 0.78022 0.08985 8.684 < 2e-16 ***
## category_code_LT01_5_count 0.91635 0.06236 14.694 < 2e-16 ***
## category_code_LT01_6_count 0.38318 0.15355 2.496 0.012904 *
## category_code_LT01_8_count -0.14149 0.27259 -0.519 0.603953
## category_code_LT01_10_count 0.10128 0.11269 0.899 0.369246
## category_code_LT01_11_count 0.40697 0.11103 3.665 0.000274 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6315, Adjusted R-squared: 0.627
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.617019725232878
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9909 -0.7769 0.0014 0.9189 3.9480
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96662 0.09109 109.418 < 2e-16 ***
## category_code_LT01_4_count 0.96587 0.07496 12.885 < 2e-16 ***
## category_code_LT01_5_count 0.92247 0.06335 14.562 < 2e-16 ***
## category_code_LT01_6_count 0.47568 0.15411 3.087 0.00214 **
## category_code_LT01_8_count -0.17482 0.27624 -0.633 0.52712
## category_code_LT01_10_count 0.11583 0.11415 1.015 0.31074
## category_code_LT01_12_count 0.11574 0.20756 0.558 0.57736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.617005852814232
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9921 -0.7800 0.0329 0.9081 3.9481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96675 0.09109 109.417 < 2e-16 ***
## category_code_LT01_4_count 0.96653 0.07482 12.918 < 2e-16 ***
## category_code_LT01_5_count 0.92406 0.06319 14.625 < 2e-16 ***
## category_code_LT01_6_count 0.48720 0.15297 3.185 0.00154 **
## category_code_LT01_8_count -0.16127 0.27657 -0.583 0.56009
## category_code_LT01_10_count 0.11559 0.11417 1.012 0.31183
## category_code_LT01_13_count 0.13281 0.24529 0.541 0.58846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.616991375660817
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9951 -0.7787 0.0400 0.8918 3.9569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97143 0.09153 108.936 < 2e-16 ***
## category_code_LT01_4_count 0.96376 0.07600 12.681 < 2e-16 ***
## category_code_LT01_5_count 0.92154 0.06355 14.501 < 2e-16 ***
## category_code_LT01_6_count 0.49655 0.15424 3.219 0.00137 **
## category_code_LT01_8_count -0.17207 0.27614 -0.623 0.53350
## category_code_LT01_10_count 0.10338 0.11734 0.881 0.37875
## category_code_LT01_14_count 0.17823 0.34015 0.524 0.60052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.616803476093567
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9935 -0.7863 0.0136 0.8928 3.9472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96709 0.09114 109.364 < 2e-16 ***
## category_code_LT01_4_count 0.97157 0.07444 13.052 < 2e-16 ***
## category_code_LT01_5_count 0.92554 0.06317 14.651 < 2e-16 ***
## category_code_LT01_6_count 0.48501 0.15313 3.167 0.00163 **
## category_code_LT01_8_count -0.17035 0.27619 -0.617 0.53765
## category_code_LT01_10_count 0.11614 0.11446 1.015 0.31075
## category_code_LT01_15_count 0.13887 0.75681 0.183 0.85449
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.61802537598987
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9928 -0.7901 0.0024 0.9180 3.9541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96816 0.09098 109.570 < 2e-16 ***
## category_code_LT01_4_count 0.96522 0.07391 13.059 < 2e-16 ***
## category_code_LT01_5_count 0.92292 0.06309 14.629 < 2e-16 ***
## category_code_LT01_6_count 0.50023 0.15316 3.266 0.00117 **
## category_code_LT01_8_count -0.18804 0.27611 -0.681 0.49617
## category_code_LT01_10_count 0.10888 0.11416 0.954 0.34069
## category_code_LT01_16_count 1.48746 1.17431 1.267 0.20588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626471348721602
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0170 -0.7383 0.0113 0.9565 3.7536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99595 0.08675 115.231 < 2e-16 ***
## category_code_LT01_4_count 0.78561 0.08971 8.757 < 2e-16 ***
## category_code_LT01_5_count 0.91782 0.06257 14.668 < 2e-16 ***
## category_code_LT01_6_count 0.40955 0.15220 2.691 0.007370 **
## category_code_LT01_8_count -0.13306 0.27296 -0.487 0.626148
## category_code_LT01_11_count 0.42171 0.11486 3.671 0.000268 ***
## category_code_LT01_12_count -0.07748 0.21197 -0.366 0.714866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626467497587391
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0146 -0.7565 0.0187 0.9601 3.7635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99523 0.08674 115.228 < 2e-16 ***
## category_code_LT01_4_count 0.78214 0.09023 8.669 < 2e-16 ***
## category_code_LT01_5_count 0.91540 0.06244 14.660 < 2e-16 ***
## category_code_LT01_6_count 0.40607 0.15173 2.676 0.007695 **
## category_code_LT01_8_count -0.13165 0.27316 -0.482 0.630053
## category_code_LT01_11_count 0.40851 0.11123 3.673 0.000266 ***
## category_code_LT01_13_count 0.08697 0.24255 0.359 0.720089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626636893802857
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0145 -0.7639 0.0297 0.9215 3.7618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99732 0.08677 115.213 < 2e-16 ***
## category_code_LT01_4_count 0.77539 0.09133 8.490 2.46e-16 ***
## category_code_LT01_5_count 0.91215 0.06276 14.535 < 2e-16 ***
## category_code_LT01_6_count 0.41362 0.15236 2.715 0.006866 **
## category_code_LT01_8_count -0.14025 0.27273 -0.514 0.607299
## category_code_LT01_11_count 0.40796 0.11111 3.672 0.000267 ***
## category_code_LT01_14_count 0.19378 0.32690 0.593 0.553605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6266
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.626371758558708
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0155 -0.7574 0.0224 0.9586 3.7617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99553 0.08675 115.222 < 2e-16 ***
## category_code_LT01_4_count 0.78520 0.09004 8.721 < 2e-16 ***
## category_code_LT01_5_count 0.91623 0.06243 14.676 < 2e-16 ***
## category_code_LT01_6_count 0.40474 0.15187 2.665 0.007950 **
## category_code_LT01_8_count -0.13726 0.27279 -0.503 0.615059
## category_code_LT01_11_count 0.41061 0.11122 3.692 0.000248 ***
## category_code_LT01_15_count 0.03884 0.74639 0.052 0.958519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.627356658411451
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0136 -0.7564 0.0288 0.9505 3.7668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99508 0.08664 115.368 < 2e-16 ***
## category_code_LT01_4_count 0.78109 0.08969 8.709 < 2e-16 ***
## category_code_LT01_5_count 0.91421 0.06236 14.661 < 2e-16 ***
## category_code_LT01_6_count 0.41764 0.15193 2.749 0.006200 **
## category_code_LT01_8_count -0.15399 0.27282 -0.564 0.572702
## category_code_LT01_11_count 0.40393 0.11106 3.637 0.000305 ***
## category_code_LT01_16_count 1.32224 1.15949 1.140 0.254690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616459269230541
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0140 -0.7920 0.0259 0.9361 4.0512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99072 0.08789 113.667 < 2e-16 ***
## category_code_LT01_4_count 0.96677 0.07566 12.777 < 2e-16 ***
## category_code_LT01_5_count 0.92101 0.06343 14.520 < 2e-16 ***
## category_code_LT01_6_count 0.50224 0.15214 3.301 0.00103 **
## category_code_LT01_8_count -0.16138 0.27688 -0.583 0.56026
## category_code_LT01_12_count 0.11821 0.20773 0.569 0.56956
## category_code_LT01_13_count 0.13681 0.24546 0.557 0.57754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6165
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616600389402518
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0143 -0.7891 0.0292 0.9153 4.0485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99338 0.08793 113.647 < 2e-16 ***
## category_code_LT01_4_count 0.96065 0.07691 12.491 < 2e-16 ***
## category_code_LT01_5_count 0.91762 0.06372 14.401 < 2e-16 ***
## category_code_LT01_6_count 0.51185 0.15285 3.349 0.000874 ***
## category_code_LT01_8_count -0.17337 0.27639 -0.627 0.530765
## category_code_LT01_12_count 0.10921 0.20838 0.524 0.600464
## category_code_LT01_14_count 0.23290 0.33221 0.701 0.483596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616274778580574
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0155 -0.7940 0.0177 0.9333 4.0507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99119 0.08791 113.649 < 2e-16 ***
## category_code_LT01_4_count 0.97077 0.07543 12.869 < 2e-16 ***
## category_code_LT01_5_count 0.92252 0.06342 14.546 < 2e-16 ***
## category_code_LT01_6_count 0.49896 0.15242 3.274 0.00114 **
## category_code_LT01_8_count -0.17114 0.27648 -0.619 0.53622
## category_code_LT01_12_count 0.12318 0.20771 0.593 0.55342
## category_code_LT01_15_count 0.20607 0.75532 0.273 0.78510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6163
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617578052823862
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0131 -0.7958 0.0193 0.9455 4.0512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99070 0.08776 113.836 < 2e-16 ***
## category_code_LT01_4_count 0.96470 0.07482 12.894 < 2e-16 ***
## category_code_LT01_5_count 0.91978 0.06333 14.524 < 2e-16 ***
## category_code_LT01_6_count 0.51410 0.15222 3.377 0.00079 ***
## category_code_LT01_8_count -0.18953 0.27638 -0.686 0.49319
## category_code_LT01_12_count 0.11985 0.20732 0.578 0.56346
## category_code_LT01_16_count 1.55062 1.17283 1.322 0.18675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616646103416439
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0152 -0.7831 0.0333 0.9016 4.0483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99354 0.08792 113.661 < 2e-16 ***
## category_code_LT01_4_count 0.95947 0.07706 12.451 < 2e-16 ***
## category_code_LT01_5_count 0.91864 0.06360 14.445 < 2e-16 ***
## category_code_LT01_6_count 0.52335 0.15142 3.456 0.000595 ***
## category_code_LT01_8_count -0.15978 0.27669 -0.577 0.563897
## category_code_LT01_13_count 0.14158 0.24526 0.577 0.564024
## category_code_LT01_14_count 0.24835 0.33090 0.751 0.453292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616275010707084
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0168 -0.7808 0.0220 0.9034 4.0506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99127 0.08791 113.651 < 2e-16 ***
## category_code_LT01_4_count 0.97096 0.07538 12.881 < 2e-16 ***
## category_code_LT01_5_count 0.92421 0.06326 14.611 < 2e-16 ***
## category_code_LT01_6_count 0.51098 0.15113 3.381 0.00078 ***
## category_code_LT01_8_count -0.15650 0.27679 -0.565 0.57206
## category_code_LT01_13_count 0.14586 0.24584 0.593 0.55325
## category_code_LT01_15_count 0.22434 0.75658 0.297 0.76696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6163
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617615664857154
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7820 0.0196 0.9440 4.0512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99073 0.08776 113.843 < 2e-16 ***
## category_code_LT01_4_count 0.96446 0.07472 12.907 < 2e-16 ***
## category_code_LT01_5_count 0.92125 0.06316 14.585 < 2e-16 ***
## category_code_LT01_6_count 0.52625 0.15095 3.486 0.000534 ***
## category_code_LT01_8_count -0.17494 0.27666 -0.632 0.527452
## category_code_LT01_13_count 0.15157 0.24507 0.618 0.536539
## category_code_LT01_16_count 1.57838 1.17330 1.345 0.179166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6176
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616434244894781
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0168 -0.7836 0.0389 0.9034 4.0478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99403 0.08795 113.639 < 2e-16 ***
## category_code_LT01_4_count 0.96441 0.07668 12.578 < 2e-16 ***
## category_code_LT01_5_count 0.92032 0.06359 14.473 < 2e-16 ***
## category_code_LT01_6_count 0.52066 0.15169 3.432 0.000649 ***
## category_code_LT01_8_count -0.16955 0.27633 -0.614 0.539774
## category_code_LT01_14_count 0.24684 0.33104 0.746 0.456239
## category_code_LT01_15_count 0.18780 0.75511 0.249 0.803694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617847377391285
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0142 -0.7833 0.0340 0.9299 4.0481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99376 0.08778 113.846 < 2e-16 ***
## category_code_LT01_4_count 0.95594 0.07625 12.536 < 2e-16 ***
## category_code_LT01_5_count 0.91689 0.06350 14.440 < 2e-16 ***
## category_code_LT01_6_count 0.53680 0.15152 3.543 0.000434 ***
## category_code_LT01_8_count -0.18927 0.27621 -0.685 0.493515
## category_code_LT01_14_count 0.27294 0.33088 0.825 0.409824
## category_code_LT01_16_count 1.60892 1.17414 1.370 0.171221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617384781568217
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0160 -0.7809 0.0267 0.9338 4.0506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99128 0.08778 113.819 < 2e-16 ***
## category_code_LT01_4_count 0.96945 0.07436 13.036 < 2e-16 ***
## category_code_LT01_5_count 0.92307 0.06315 14.617 < 2e-16 ***
## category_code_LT01_6_count 0.52309 0.15120 3.460 0.000588 ***
## category_code_LT01_8_count -0.18532 0.27633 -0.671 0.502746
## category_code_LT01_15_count 0.22122 0.75429 0.293 0.769431
## category_code_LT01_16_count 1.56431 1.17343 1.333 0.183116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.629191428494852
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9836 -0.7573 0.0154 0.9693 3.7140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97242 0.08963 111.264 < 2e-16 ***
## category_code_LT01_4_count 0.76234 0.09006 8.465 2.97e-16 ***
## category_code_LT01_5_count 0.90355 0.06167 14.652 < 2e-16 ***
## category_code_LT01_6_count 0.37016 0.15307 2.418 0.015956 *
## category_code_LT01_9_count 0.40120 0.22446 1.787 0.074486 .
## category_code_LT01_10_count 0.07657 0.11312 0.677 0.498782
## category_code_LT01_11_count 0.39141 0.11108 3.524 0.000465 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6337, Adjusted R-squared: 0.6292
## F-statistic: 141.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.620020505044022
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9786 -0.7546 0.0269 0.9382 3.9820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96501 0.09071 109.860 < 2e-16 ***
## category_code_LT01_4_count 0.93695 0.07596 12.335 < 2e-16 ***
## category_code_LT01_5_count 0.90708 0.06263 14.484 < 2e-16 ***
## category_code_LT01_6_count 0.45654 0.15362 2.972 0.00311 **
## category_code_LT01_9_count 0.46840 0.22638 2.069 0.03906 *
## category_code_LT01_10_count 0.08634 0.11451 0.754 0.45121
## category_code_LT01_12_count 0.10670 0.20665 0.516 0.60584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.620200003998086
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9798 -0.7548 0.0323 0.9141 3.9835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96526 0.09068 109.889 < 2e-16 ***
## category_code_LT01_4_count 0.93429 0.07598 12.297 < 2e-16 ***
## category_code_LT01_5_count 0.90838 0.06242 14.553 < 2e-16 ***
## category_code_LT01_6_count 0.46747 0.15244 3.067 0.00228 **
## category_code_LT01_9_count 0.47948 0.22673 2.115 0.03495 *
## category_code_LT01_10_count 0.08475 0.11453 0.740 0.45967
## category_code_LT01_13_count 0.17255 0.24431 0.706 0.48036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.619947326151473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9823 -0.7609 0.0362 0.9263 3.9885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96884 0.09116 109.361 < 2e-16 ***
## category_code_LT01_4_count 0.93650 0.07685 12.186 < 2e-16 ***
## category_code_LT01_5_count 0.90689 0.06280 14.440 < 2e-16 ***
## category_code_LT01_6_count 0.47457 0.15380 3.086 0.00215 **
## category_code_LT01_9_count 0.46508 0.22666 2.052 0.04071 *
## category_code_LT01_10_count 0.07699 0.11754 0.655 0.51277
## category_code_LT01_14_count 0.14069 0.33921 0.415 0.67850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.6199
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.619858215978146
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9813 -0.7625 0.0465 0.9245 3.9819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96562 0.09075 109.816 < 2e-16 ***
## category_code_LT01_4_count 0.94134 0.07553 12.463 < 2e-16 ***
## category_code_LT01_5_count 0.91007 0.06242 14.579 < 2e-16 ***
## category_code_LT01_6_count 0.46469 0.15264 3.044 0.00246 **
## category_code_LT01_9_count 0.47127 0.22650 2.081 0.03798 *
## category_code_LT01_10_count 0.08589 0.11484 0.748 0.45488
## category_code_LT01_15_count 0.17985 0.75407 0.239 0.81159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6199
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.620828209409534
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9801 -0.7575 0.0511 0.9285 3.9869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96632 0.09062 109.985 < 2e-16 ***
## category_code_LT01_4_count 0.93715 0.07493 12.507 < 2e-16 ***
## category_code_LT01_5_count 0.90737 0.06237 14.548 < 2e-16 ***
## category_code_LT01_6_count 0.47900 0.15271 3.137 0.00181 **
## category_code_LT01_9_count 0.45813 0.22635 2.024 0.04351 *
## category_code_LT01_10_count 0.08066 0.11452 0.704 0.48156
## category_code_LT01_16_count 1.34025 1.16960 1.146 0.25239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6254, Adjusted R-squared: 0.6208
## F-statistic: 136.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.628949387849983
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0011 -0.7716 0.0240 0.9499 3.7726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98893 0.08648 115.503 < 2e-16 ***
## category_code_LT01_4_count 0.76570 0.08995 8.512 < 2e-16 ***
## category_code_LT01_5_count 0.90500 0.06188 14.624 < 2e-16 ***
## category_code_LT01_6_count 0.39078 0.15181 2.574 0.01034 *
## category_code_LT01_9_count 0.41828 0.22300 1.876 0.06129 .
## category_code_LT01_11_count 0.40444 0.11486 3.521 0.00047 ***
## category_code_LT01_12_count -0.07831 0.21110 -0.371 0.71082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6334, Adjusted R-squared: 0.6289
## F-statistic: 141.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.629038541302392
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9982 -0.7553 0.0378 0.9563 3.7837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98800 0.08647 115.513 < 2e-16 ***
## category_code_LT01_4_count 0.76051 0.09051 8.402 4.75e-16 ***
## category_code_LT01_5_count 0.90221 0.06171 14.621 < 2e-16 ***
## category_code_LT01_6_count 0.38746 0.15129 2.561 0.010735 *
## category_code_LT01_9_count 0.42603 0.22340 1.907 0.057096 .
## category_code_LT01_11_count 0.38974 0.11131 3.501 0.000505 ***
## category_code_LT01_13_count 0.12227 0.24181 0.506 0.613346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6335, Adjusted R-squared: 0.629
## F-statistic: 141.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.628998680728315
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9987 -0.7556 0.0353 0.9569 3.7805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98989 0.08653 115.456 < 2e-16 ***
## category_code_LT01_4_count 0.75819 0.09145 8.291 1.09e-15 ***
## category_code_LT01_5_count 0.90019 0.06203 14.511 < 2e-16 ***
## category_code_LT01_6_count 0.39283 0.15201 2.584 0.010046 *
## category_code_LT01_9_count 0.41172 0.22355 1.842 0.066122 .
## category_code_LT01_11_count 0.39166 0.11114 3.524 0.000465 ***
## category_code_LT01_14_count 0.14713 0.32665 0.450 0.652607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6335, Adjusted R-squared: 0.629
## F-statistic: 141.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.628851878629556
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9994 -0.7660 0.0421 0.9605 3.7810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98845 0.08648 115.496 < 2e-16 ***
## category_code_LT01_4_count 0.76491 0.09030 8.470 2.86e-16 ***
## category_code_LT01_5_count 0.90327 0.06171 14.639 < 2e-16 ***
## category_code_LT01_6_count 0.38551 0.15146 2.545 0.011222 *
## category_code_LT01_9_count 0.41948 0.22308 1.880 0.060646 .
## category_code_LT01_11_count 0.39297 0.11128 3.531 0.000452 ***
## category_code_LT01_15_count 0.06896 0.74409 0.093 0.926198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.372 on 491 degrees of freedom
## Multiple R-squared: 0.6333, Adjusted R-squared: 0.6289
## F-statistic: 141.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.629641468578712
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9976 -0.7545 0.0474 0.9644 3.7849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98806 0.08639 115.613 < 2e-16 ***
## category_code_LT01_4_count 0.76196 0.08994 8.472 2.83e-16 ***
## category_code_LT01_5_count 0.90112 0.06166 14.614 < 2e-16 ***
## category_code_LT01_6_count 0.39744 0.15155 2.623 0.008998 **
## category_code_LT01_9_count 0.40818 0.22303 1.830 0.067826 .
## category_code_LT01_11_count 0.38793 0.11110 3.492 0.000523 ***
## category_code_LT01_16_count 1.18708 1.15551 1.027 0.304772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.371 on 491 degrees of freedom
## Multiple R-squared: 0.6341, Adjusted R-squared: 0.6296
## F-statistic: 141.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.619981733133562
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9948 -0.7720 0.0314 0.9150 4.0594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98249 0.08751 114.076 < 2e-16 ***
## category_code_LT01_4_count 0.93189 0.07689 12.119 < 2e-16 ***
## category_code_LT01_5_count 0.90523 0.06265 14.449 < 2e-16 ***
## category_code_LT01_6_count 0.47571 0.15173 3.135 0.00182 **
## category_code_LT01_9_count 0.49803 0.22513 2.212 0.02741 *
## category_code_LT01_12_count 0.10644 0.20668 0.515 0.60678
## category_code_LT01_13_count 0.17589 0.24430 0.720 0.47188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.619800749520036
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9958 -0.7744 0.0503 0.9479 4.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98484 0.08759 113.994 < 2e-16 ***
## category_code_LT01_4_count 0.93213 0.07776 11.988 < 2e-16 ***
## category_code_LT01_5_count 0.90320 0.06296 14.346 < 2e-16 ***
## category_code_LT01_6_count 0.48312 0.15257 3.167 0.00164 **
## category_code_LT01_9_count 0.47983 0.22545 2.128 0.03381 *
## category_code_LT01_12_count 0.10152 0.20742 0.489 0.62473
## category_code_LT01_14_count 0.17689 0.33171 0.533 0.59407
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6198
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.619654433806478
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9966 -0.7779 0.0413 0.9327 4.0588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98308 0.08754 114.038 < 2e-16 ***
## category_code_LT01_4_count 0.93803 0.07659 12.248 < 2e-16 ***
## category_code_LT01_5_count 0.90688 0.06266 14.473 < 2e-16 ***
## category_code_LT01_6_count 0.47201 0.15205 3.104 0.00202 **
## category_code_LT01_9_count 0.49012 0.22490 2.179 0.02978 *
## category_code_LT01_12_count 0.11246 0.20671 0.544 0.58666
## category_code_LT01_15_count 0.23230 0.75209 0.309 0.75755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6242, Adjusted R-squared: 0.6197
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.620660713113722
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9943 -0.7722 0.0431 0.9437 4.0592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98268 0.08743 114.184 < 2e-16 ***
## category_code_LT01_4_count 0.93432 0.07590 12.310 < 2e-16 ***
## category_code_LT01_5_count 0.90414 0.06260 14.443 < 2e-16 ***
## category_code_LT01_6_count 0.48645 0.15191 3.202 0.00145 **
## category_code_LT01_9_count 0.47510 0.22486 2.113 0.03512 *
## category_code_LT01_12_count 0.10903 0.20640 0.528 0.59756
## category_code_LT01_16_count 1.38114 1.16805 1.182 0.23761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6252, Adjusted R-squared: 0.6207
## F-statistic: 136.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.620033888093685
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9964 -0.7724 0.0330 0.9053 4.0570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98484 0.08756 114.036 < 2e-16 ***
## category_code_LT01_4_count 0.92787 0.07803 11.891 < 2e-16 ***
## category_code_LT01_5_count 0.90403 0.06279 14.397 < 2e-16 ***
## category_code_LT01_6_count 0.49382 0.15110 3.268 0.00116 **
## category_code_LT01_9_count 0.49011 0.22573 2.171 0.03039 *
## category_code_LT01_13_count 0.17959 0.24416 0.736 0.46237
## category_code_LT01_14_count 0.19053 0.33034 0.577 0.56436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.619868646228183
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9974 -0.7725 0.0273 0.8972 4.0589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98298 0.08752 114.070 < 2e-16 ***
## category_code_LT01_4_count 0.93454 0.07670 12.184 < 2e-16 ***
## category_code_LT01_5_count 0.90826 0.06245 14.544 < 2e-16 ***
## category_code_LT01_6_count 0.48286 0.15072 3.204 0.00145 **
## category_code_LT01_9_count 0.50174 0.22519 2.228 0.02633 *
## category_code_LT01_13_count 0.18520 0.24470 0.757 0.44950
## category_code_LT01_15_count 0.25992 0.75321 0.345 0.73018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.6199
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.620908611020936
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9949 -0.7712 0.0288 0.9033 4.0593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98254 0.08740 114.221 < 2e-16 ***
## category_code_LT01_4_count 0.93061 0.07597 12.250 < 2e-16 ***
## category_code_LT01_5_count 0.90530 0.06239 14.509 < 2e-16 ***
## category_code_LT01_6_count 0.49758 0.15060 3.304 0.00102 **
## category_code_LT01_9_count 0.48636 0.22510 2.161 0.03121 *
## category_code_LT01_13_count 0.18905 0.24400 0.775 0.43883
## category_code_LT01_16_count 1.41460 1.16818 1.211 0.22650
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.6209
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.619679426060973
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9982 -0.7750 0.0382 0.9182 4.0564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98546 0.08760 113.994 < 2e-16 ***
## category_code_LT01_4_count 0.93491 0.07758 12.051 < 2e-16 ***
## category_code_LT01_5_count 0.90586 0.06280 14.424 < 2e-16 ***
## category_code_LT01_6_count 0.49080 0.15142 3.241 0.00127 **
## category_code_LT01_9_count 0.48207 0.22552 2.138 0.03304 *
## category_code_LT01_14_count 0.18938 0.33054 0.573 0.56695
## category_code_LT01_15_count 0.21649 0.75203 0.288 0.77357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6197
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.620770116264852
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9957 -0.7748 0.0375 0.9258 4.0566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98529 0.08747 114.156 < 2e-16 ***
## category_code_LT01_4_count 0.92906 0.07708 12.053 < 2e-16 ***
## category_code_LT01_5_count 0.90246 0.06275 14.382 < 2e-16 ***
## category_code_LT01_6_count 0.50621 0.15132 3.345 0.000885 ***
## category_code_LT01_9_count 0.46529 0.22550 2.063 0.039606 *
## category_code_LT01_14_count 0.21443 0.33057 0.649 0.516849
## category_code_LT01_16_count 1.43042 1.16979 1.223 0.221989
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6253, Adjusted R-squared: 0.6208
## F-statistic: 136.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.620527000928462
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9970 -0.7805 0.0286 0.9174 4.0587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98321 0.08744 114.176 < 2e-16 ***
## category_code_LT01_4_count 0.93781 0.07553 12.416 < 2e-16 ***
## category_code_LT01_5_count 0.90726 0.06240 14.539 < 2e-16 ***
## category_code_LT01_6_count 0.49411 0.15090 3.274 0.00113 **
## category_code_LT01_9_count 0.47813 0.22491 2.126 0.03401 *
## category_code_LT01_15_count 0.24454 0.75127 0.325 0.74494
## category_code_LT01_16_count 1.39522 1.16851 1.194 0.23305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6251, Adjusted R-squared: 0.6205
## F-statistic: 136.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626903069273991
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9914 -0.7352 -0.0078 0.9307 3.6660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97330 0.08991 110.928 < 2e-16 ***
## category_code_LT01_4_count 0.77919 0.08984 8.673 < 2e-16 ***
## category_code_LT01_5_count 0.91358 0.06189 14.762 < 2e-16 ***
## category_code_LT01_6_count 0.38482 0.15391 2.500 0.012733 *
## category_code_LT01_10_count 0.10119 0.11271 0.898 0.369732
## category_code_LT01_11_count 0.42043 0.11478 3.663 0.000276 ***
## category_code_LT01_12_count -0.08568 0.21172 -0.405 0.685880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626876346750394
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9894 -0.7444 -0.0035 0.9339 3.6786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97301 0.08991 110.925 < 2e-16 ***
## category_code_LT01_4_count 0.77586 0.09032 8.590 < 2e-16 ***
## category_code_LT01_5_count 0.91101 0.06172 14.761 < 2e-16 ***
## category_code_LT01_6_count 0.38137 0.15349 2.485 0.013303 *
## category_code_LT01_10_count 0.09897 0.11275 0.878 0.380477
## category_code_LT01_11_count 0.40617 0.11121 3.652 0.000288 ***
## category_code_LT01_13_count 0.08684 0.24216 0.359 0.720042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626892468385715
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9912 -0.7535 -0.0081 0.9368 3.6847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97638 0.09033 110.439 < 2e-16 ***
## category_code_LT01_4_count 0.77290 0.09130 8.465 2.97e-16 ***
## category_code_LT01_5_count 0.90883 0.06211 14.634 < 2e-16 ***
## category_code_LT01_6_count 0.38791 0.15481 2.506 0.012542 *
## category_code_LT01_10_count 0.08982 0.11586 0.775 0.438578
## category_code_LT01_11_count 0.40704 0.11108 3.664 0.000275 ***
## category_code_LT01_14_count 0.13002 0.33591 0.387 0.698874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.626778895159716
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9897 -0.7448 -0.0102 0.9378 3.6753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97295 0.08994 110.885 < 2e-16 ***
## category_code_LT01_4_count 0.77934 0.09014 8.646 < 2e-16 ***
## category_code_LT01_5_count 0.91157 0.06172 14.770 < 2e-16 ***
## category_code_LT01_6_count 0.38005 0.15354 2.475 0.013648 *
## category_code_LT01_10_count 0.10039 0.11301 0.888 0.374810
## category_code_LT01_11_count 0.40873 0.11118 3.676 0.000263 ***
## category_code_LT01_15_count -0.01422 0.74798 -0.019 0.984839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6313, Adjusted R-squared: 0.6268
## F-statistic: 140.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.627631174406663
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9891 -0.7529 0.0064 0.9363 3.6867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97396 0.08982 111.042 < 2e-16 ***
## category_code_LT01_4_count 0.77528 0.08983 8.631 < 2e-16 ***
## category_code_LT01_5_count 0.90924 0.06167 14.743 < 2e-16 ***
## category_code_LT01_6_count 0.39282 0.15377 2.555 0.010931 *
## category_code_LT01_10_count 0.09306 0.11278 0.825 0.409706
## category_code_LT01_11_count 0.40254 0.11104 3.625 0.000319 ***
## category_code_LT01_16_count 1.22932 1.15944 1.060 0.289545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6321, Adjusted R-squared: 0.6276
## F-statistic: 140.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616952176849523
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9851 -0.7625 -0.0017 0.9137 3.9528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96541 0.09107 109.424 <2e-16 ***
## category_code_LT01_4_count 0.95896 0.07597 12.623 <2e-16 ***
## category_code_LT01_5_count 0.91584 0.06272 14.601 <2e-16 ***
## category_code_LT01_6_count 0.47416 0.15407 3.078 0.0022 **
## category_code_LT01_10_count 0.11260 0.11421 0.986 0.3247
## category_code_LT01_12_count 0.10809 0.20756 0.521 0.6028
## category_code_LT01_13_count 0.13726 0.24501 0.560 0.5756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616880836417137
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7608 0.0098 0.9128 3.9604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96955 0.09153 108.926 < 2e-16 ***
## category_code_LT01_4_count 0.95766 0.07696 12.443 < 2e-16 ***
## category_code_LT01_5_count 0.91349 0.06309 14.480 < 2e-16 ***
## category_code_LT01_6_count 0.48269 0.15554 3.103 0.00202 **
## category_code_LT01_10_count 0.10185 0.11736 0.868 0.38589
## category_code_LT01_12_count 0.10338 0.20822 0.496 0.61977
## category_code_LT01_14_count 0.16097 0.34135 0.472 0.63745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6215, Adjusted R-squared: 0.6169
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.616735692676746
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9861 -0.7657 -0.0050 0.9180 3.9520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96569 0.09112 109.368 < 2e-16 ***
## category_code_LT01_4_count 0.96386 0.07571 12.731 < 2e-16 ***
## category_code_LT01_5_count 0.91694 0.06273 14.617 < 2e-16 ***
## category_code_LT01_6_count 0.47131 0.15424 3.056 0.00237 **
## category_code_LT01_10_count 0.11302 0.11451 0.987 0.32415
## category_code_LT01_12_count 0.11247 0.20758 0.542 0.58821
## category_code_LT01_15_count 0.14432 0.75705 0.191 0.84889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617882272029367
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9849 -0.7614 0.0049 0.9300 3.9588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96657 0.09097 109.563 < 2e-16 ***
## category_code_LT01_4_count 0.95800 0.07515 12.748 < 2e-16 ***
## category_code_LT01_5_count 0.91390 0.06267 14.583 < 2e-16 ***
## category_code_LT01_6_count 0.48606 0.15425 3.151 0.00173 **
## category_code_LT01_10_count 0.10602 0.11423 0.928 0.35376
## category_code_LT01_12_count 0.10961 0.20722 0.529 0.59710
## category_code_LT01_16_count 1.44127 1.17299 1.229 0.21977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616952177425453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9892 -0.7598 0.0082 0.8959 3.9618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97020 0.09151 108.949 < 2e-16 ***
## category_code_LT01_4_count 0.95615 0.07709 12.403 < 2e-16 ***
## category_code_LT01_5_count 0.91480 0.06292 14.538 < 2e-16 ***
## category_code_LT01_6_count 0.49437 0.15416 3.207 0.00143 **
## category_code_LT01_10_count 0.10005 0.11741 0.852 0.39458
## category_code_LT01_13_count 0.14238 0.24491 0.581 0.56126
## category_code_LT01_14_count 0.17713 0.34015 0.521 0.60279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.61677703438123
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9878 -0.7686 -0.0066 0.9018 3.9524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96598 0.09112 109.377 < 2e-16 ***
## category_code_LT01_4_count 0.96346 0.07563 12.739 < 2e-16 ***
## category_code_LT01_5_count 0.91885 0.06252 14.697 < 2e-16 ***
## category_code_LT01_6_count 0.48273 0.15305 3.154 0.00171 **
## category_code_LT01_10_count 0.11241 0.11453 0.981 0.32684
## category_code_LT01_13_count 0.14450 0.24546 0.589 0.55635
## category_code_LT01_15_count 0.16378 0.75838 0.216 0.82910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617965185942663
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9864 -0.7634 -0.0020 0.9183 3.9594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96684 0.09096 109.577 < 2e-16 ***
## category_code_LT01_4_count 0.95710 0.07504 12.755 < 2e-16 ***
## category_code_LT01_5_count 0.91558 0.06246 14.659 < 2e-16 ***
## category_code_LT01_6_count 0.49774 0.15308 3.252 0.00123 **
## category_code_LT01_10_count 0.10529 0.11424 0.922 0.35716
## category_code_LT01_13_count 0.15213 0.24473 0.622 0.53450
## category_code_LT01_16_count 1.47229 1.17359 1.255 0.21025
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616713565209807
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9903 -0.7683 -0.0004 0.9035 3.9606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97041 0.09156 108.890 < 2e-16 ***
## category_code_LT01_4_count 0.96182 0.07670 12.540 < 2e-16 ***
## category_code_LT01_5_count 0.91608 0.06293 14.557 < 2e-16 ***
## category_code_LT01_6_count 0.49183 0.15432 3.187 0.00153 **
## category_code_LT01_10_count 0.10088 0.11770 0.857 0.39182
## category_code_LT01_14_count 0.17521 0.34024 0.515 0.60682
## category_code_LT01_15_count 0.13563 0.75688 0.179 0.85786
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6213, Adjusted R-squared: 0.6167
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617947967011204
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9893 -0.7658 0.0090 0.9111 3.9698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97216 0.09141 109.095 < 2e-16 ***
## category_code_LT01_4_count 0.95371 0.07631 12.499 < 2e-16 ***
## category_code_LT01_5_count 0.91221 0.06288 14.507 < 2e-16 ***
## category_code_LT01_6_count 0.50850 0.15443 3.293 0.00106 **
## category_code_LT01_10_count 0.09096 0.11753 0.774 0.43935
## category_code_LT01_14_count 0.20551 0.34053 0.604 0.54644
## category_code_LT01_16_count 1.49586 1.17575 1.272 0.20389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.6179
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617700175386851
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9876 -0.7750 -0.0052 0.9097 3.9584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.96714 0.09101 109.514 < 2e-16 ***
## category_code_LT01_4_count 0.96293 0.07466 12.898 < 2e-16 ***
## category_code_LT01_5_count 0.91697 0.06246 14.680 < 2e-16 ***
## category_code_LT01_6_count 0.49483 0.15322 3.229 0.00132 **
## category_code_LT01_10_count 0.10593 0.11455 0.925 0.35553
## category_code_LT01_15_count 0.16175 0.75621 0.214 0.83072
## category_code_LT01_16_count 1.45322 1.17371 1.238 0.21625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.626407517886675
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0121 -0.7475 0.0185 0.9615 3.7554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99452 0.08672 115.246 < 2e-16 ***
## category_code_LT01_4_count 0.78087 0.09020 8.657 < 2e-16 ***
## category_code_LT01_5_count 0.91285 0.06195 14.736 < 2e-16 ***
## category_code_LT01_6_count 0.40787 0.15213 2.681 0.007585 **
## category_code_LT01_11_count 0.42125 0.11498 3.664 0.000276 ***
## category_code_LT01_12_count -0.08297 0.21184 -0.392 0.695465
## category_code_LT01_13_count 0.09496 0.24223 0.392 0.695205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.626578672428374
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0119 -0.7414 0.0320 0.9252 3.7526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99669 0.08676 115.227 < 2e-16 ***
## category_code_LT01_4_count 0.77396 0.09133 8.475 2.77e-16 ***
## category_code_LT01_5_count 0.90940 0.06226 14.606 < 2e-16 ***
## category_code_LT01_6_count 0.41599 0.15283 2.722 0.006720 **
## category_code_LT01_11_count 0.42216 0.11480 3.678 0.000262 ***
## category_code_LT01_12_count -0.09208 0.21244 -0.433 0.664883
## category_code_LT01_14_count 0.20181 0.32790 0.615 0.538529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6266
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.626291394547044
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0128 -0.7368 0.0185 0.9595 3.7535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99479 0.08673 115.234 < 2e-16 ***
## category_code_LT01_4_count 0.78435 0.09003 8.712 < 2e-16 ***
## category_code_LT01_5_count 0.91348 0.06195 14.746 < 2e-16 ***
## category_code_LT01_6_count 0.40633 0.15227 2.668 0.007874 **
## category_code_LT01_11_count 0.42355 0.11505 3.682 0.000258 ***
## category_code_LT01_12_count -0.08143 0.21198 -0.384 0.701049
## category_code_LT01_15_count 0.02445 0.74691 0.033 0.973901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.627225218785827
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0105 -0.7436 0.0322 0.9525 3.7586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99420 0.08663 115.370 < 2e-16 ***
## category_code_LT01_4_count 0.78010 0.08969 8.698 < 2e-16 ***
## category_code_LT01_5_count 0.91097 0.06190 14.716 < 2e-16 ***
## category_code_LT01_6_count 0.41831 0.15229 2.747 0.006241 **
## category_code_LT01_11_count 0.41702 0.11483 3.632 0.000311 ***
## category_code_LT01_12_count -0.08067 0.21158 -0.381 0.703162
## category_code_LT01_16_count 1.28483 1.15799 1.110 0.267744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6317, Adjusted R-squared: 0.6272
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.626550791898078
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0092 -0.7410 0.0368 0.9308 3.7641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99577 0.08675 115.227 < 2e-16 ***
## category_code_LT01_4_count 0.77078 0.09182 8.394 5.05e-16 ***
## category_code_LT01_5_count 0.90687 0.06213 14.596 < 2e-16 ***
## category_code_LT01_6_count 0.41131 0.15225 2.701 0.007142 **
## category_code_LT01_11_count 0.40688 0.11130 3.656 0.000284 ***
## category_code_LT01_13_count 0.09416 0.24215 0.389 0.697557
## category_code_LT01_14_count 0.19112 0.32688 0.585 0.559035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6311, Adjusted R-squared: 0.6266
## F-statistic: 140 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.626294722247791
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0104 -0.7436 0.0283 0.9623 3.7641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99403 0.08673 115.235 < 2e-16 ***
## category_code_LT01_4_count 0.78030 0.09058 8.614 < 2e-16 ***
## category_code_LT01_5_count 0.91102 0.06178 14.747 < 2e-16 ***
## category_code_LT01_6_count 0.40249 0.15176 2.652 0.008258 **
## category_code_LT01_11_count 0.40932 0.11143 3.673 0.000266 ***
## category_code_LT01_13_count 0.09462 0.24275 0.390 0.696868
## category_code_LT01_15_count 0.05378 0.74802 0.072 0.942719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.627253984226748
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7519 0.0355 0.9547 3.7693
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99341 0.08662 115.374 < 2e-16 ***
## category_code_LT01_4_count 0.77592 0.09020 8.602 < 2e-16 ***
## category_code_LT01_5_count 0.90838 0.06173 14.716 < 2e-16 ***
## category_code_LT01_6_count 0.41508 0.15182 2.734 0.006482 **
## category_code_LT01_11_count 0.40278 0.11126 3.620 0.000325 ***
## category_code_LT01_13_count 0.10364 0.24209 0.428 0.668761
## category_code_LT01_16_count 1.30520 1.15874 1.126 0.260548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6318, Adjusted R-squared: 0.6273
## F-statistic: 140.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.626436914912337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0100 -0.7472 0.0216 0.9277 3.7621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99603 0.08676 115.215 < 2e-16 ***
## category_code_LT01_4_count 0.77422 0.09163 8.450 3.34e-16 ***
## category_code_LT01_5_count 0.90756 0.06214 14.606 < 2e-16 ***
## category_code_LT01_6_count 0.40980 0.15239 2.689 0.00741 **
## category_code_LT01_11_count 0.40936 0.11127 3.679 0.00026 ***
## category_code_LT01_14_count 0.19033 0.32696 0.582 0.56074
## category_code_LT01_15_count 0.02871 0.74636 0.038 0.96933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6309, Adjusted R-squared: 0.6264
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.62743241159668
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0075 -0.7581 0.0350 0.9111 3.7673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99562 0.08664 115.364 < 2e-16 ***
## category_code_LT01_4_count 0.76874 0.09135 8.415 4.32e-16 ***
## category_code_LT01_5_count 0.90453 0.06209 14.568 < 2e-16 ***
## category_code_LT01_6_count 0.42319 0.15247 2.776 0.005721 **
## category_code_LT01_11_count 0.40241 0.11113 3.621 0.000324 ***
## category_code_LT01_14_count 0.21155 0.32700 0.647 0.517984
## category_code_LT01_16_count 1.32883 1.15949 1.146 0.252333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6274
## F-statistic: 140.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.627119280113921
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7399 0.0356 0.9535 3.7672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99372 0.08663 115.360 < 2e-16 ***
## category_code_LT01_4_count 0.77946 0.09003 8.658 < 2e-16 ***
## category_code_LT01_5_count 0.90919 0.06173 14.729 < 2e-16 ***
## category_code_LT01_6_count 0.41308 0.15193 2.719 0.006783 **
## category_code_LT01_11_count 0.40535 0.11123 3.644 0.000297 ***
## category_code_LT01_15_count 0.05697 0.74587 0.076 0.939148
## category_code_LT01_16_count 1.28909 1.15856 1.113 0.266397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.375 on 491 degrees of freedom
## Multiple R-squared: 0.6316, Adjusted R-squared: 0.6271
## F-statistic: 140.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616570317372917
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0078 -0.7810 0.0053 0.8999 4.0504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99144 0.08790 113.665 < 2e-16 ***
## category_code_LT01_4_count 0.95321 0.07794 12.230 < 2e-16 ***
## category_code_LT01_5_count 0.91103 0.06311 14.436 < 2e-16 ***
## category_code_LT01_6_count 0.50961 0.15277 3.336 0.000915 ***
## category_code_LT01_12_count 0.10131 0.20839 0.486 0.627078
## category_code_LT01_13_count 0.14597 0.24500 0.596 0.551601
## category_code_LT01_14_count 0.23062 0.33218 0.694 0.487842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6166
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616265800285632
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0091 -0.7814 0.0106 0.9180 4.0526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98930 0.08788 113.669 < 2e-16 ***
## category_code_LT01_4_count 0.96267 0.07659 12.569 < 2e-16 ***
## category_code_LT01_5_count 0.91597 0.06279 14.589 < 2e-16 ***
## category_code_LT01_6_count 0.49662 0.15234 3.260 0.00119 **
## category_code_LT01_12_count 0.11524 0.20769 0.555 0.57923
## category_code_LT01_13_count 0.14969 0.24555 0.610 0.54240
## category_code_LT01_15_count 0.22950 0.75670 0.303 0.76179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6209, Adjusted R-squared: 0.6163
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617528338772129
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0061 -0.7782 0.0167 0.9486 4.0533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98863 0.08774 113.847 < 2e-16 ***
## category_code_LT01_4_count 0.95670 0.07590 12.604 < 2e-16 ***
## category_code_LT01_5_count 0.91258 0.06272 14.550 < 2e-16 ***
## category_code_LT01_6_count 0.51159 0.15214 3.363 0.000832 ***
## category_code_LT01_12_count 0.11120 0.20733 0.536 0.591959
## category_code_LT01_13_count 0.15608 0.24484 0.637 0.524108
## category_code_LT01_16_count 1.53394 1.17198 1.309 0.191198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616343597745127
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0091 -0.7814 0.0000 0.9038 4.0501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99182 0.08793 113.638 < 2e-16 ***
## category_code_LT01_4_count 0.95803 0.07767 12.335 < 2e-16 ***
## category_code_LT01_5_count 0.91231 0.06312 14.454 < 2e-16 ***
## category_code_LT01_6_count 0.50612 0.15306 3.307 0.00101 **
## category_code_LT01_12_count 0.10634 0.20840 0.510 0.61008
## category_code_LT01_14_count 0.22814 0.33233 0.686 0.49273
## category_code_LT01_15_count 0.19196 0.75534 0.254 0.79950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6163
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.61766627505538
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7810 -0.0085 0.9158 4.0505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99141 0.08778 113.829 < 2e-16 ***
## category_code_LT01_4_count 0.95025 0.07721 12.308 < 2e-16 ***
## category_code_LT01_5_count 0.90846 0.06305 14.408 < 2e-16 ***
## category_code_LT01_6_count 0.52194 0.15288 3.414 0.000693 ***
## category_code_LT01_12_count 0.10122 0.20802 0.487 0.626774
## category_code_LT01_14_count 0.25380 0.33220 0.764 0.445245
## category_code_LT01_16_count 1.55746 1.17284 1.328 0.184817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617280667268458
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0075 -0.7838 -0.0015 0.9611 4.0528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98907 0.08776 113.818 < 2e-16 ***
## category_code_LT01_4_count 0.96153 0.07567 12.707 < 2e-16 ***
## category_code_LT01_5_count 0.91396 0.06273 14.571 < 2e-16 ***
## category_code_LT01_6_count 0.50762 0.15240 3.331 0.000931 ***
## category_code_LT01_12_count 0.11657 0.20734 0.562 0.574215
## category_code_LT01_15_count 0.22430 0.75450 0.297 0.766378
## category_code_LT01_16_count 1.51665 1.17200 1.294 0.196250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616447557664737
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0103 -0.7780 0.0240 0.8888 4.0498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99208 0.08791 113.665 < 2e-16 ***
## category_code_LT01_4_count 0.95574 0.07788 12.271 < 2e-16 ***
## category_code_LT01_5_count 0.91366 0.06295 14.513 < 2e-16 ***
## category_code_LT01_6_count 0.51747 0.15157 3.414 0.000693 ***
## category_code_LT01_13_count 0.15394 0.24539 0.627 0.530736
## category_code_LT01_14_count 0.24338 0.33098 0.735 0.462492
## category_code_LT01_15_count 0.21278 0.75649 0.281 0.778617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6211, Adjusted R-squared: 0.6164
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617818884858799
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0069 -0.7811 0.0100 0.8989 4.0503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99160 0.08775 113.862 < 2e-16 ***
## category_code_LT01_4_count 0.94735 0.07740 12.239 < 2e-16 ***
## category_code_LT01_5_count 0.90953 0.06289 14.462 < 2e-16 ***
## category_code_LT01_6_count 0.53334 0.15139 3.523 0.000467 ***
## category_code_LT01_13_count 0.16095 0.24463 0.658 0.510870
## category_code_LT01_14_count 0.26900 0.33082 0.813 0.416543
## category_code_LT01_16_count 1.59186 1.17316 1.357 0.175435
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617387902115378
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7787 0.0073 0.9285 4.0527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98919 0.08775 113.839 < 2e-16 ***
## category_code_LT01_4_count 0.96000 0.07565 12.690 < 2e-16 ***
## category_code_LT01_5_count 0.91576 0.06252 14.649 < 2e-16 ***
## category_code_LT01_6_count 0.51964 0.15107 3.440 0.000632 ***
## category_code_LT01_13_count 0.16520 0.24524 0.674 0.500850
## category_code_LT01_15_count 0.24760 0.75572 0.328 0.743326
## category_code_LT01_16_count 1.55055 1.17252 1.322 0.186648
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_6_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617540401509149
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0085 -0.7816 0.0231 0.9045 4.0498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99206 0.08778 113.829 < 2e-16 ***
## category_code_LT01_4_count 0.95318 0.07701 12.377 < 2e-16 ***
## category_code_LT01_5_count 0.91110 0.06290 14.485 < 2e-16 ***
## category_code_LT01_6_count 0.52995 0.15166 3.494 0.000518 ***
## category_code_LT01_14_count 0.26679 0.33098 0.806 0.420587
## category_code_LT01_15_count 0.20666 0.75418 0.274 0.784187
## category_code_LT01_16_count 1.57301 1.17322 1.341 0.180616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count 0.620299729747341
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0066 -0.7686 -0.0138 0.8642 3.9310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97904 0.09069 110.040 <2e-16 ***
## category_code_LT01_4_count 0.94684 0.07423 12.756 <2e-16 ***
## category_code_LT01_5_count 0.92713 0.06275 14.775 <2e-16 ***
## category_code_LT01_7_count 0.48218 0.15517 3.107 0.0020 **
## category_code_LT01_8_count -0.18115 0.27497 -0.659 0.5103
## category_code_LT01_9_count 0.43997 0.22720 1.937 0.0534 .
## category_code_LT01_10_count 0.11999 0.11324 1.060 0.2898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6249, Adjusted R-squared: 0.6203
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count 0.62801977918778
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0274 -0.7477 0.0283 0.9025 3.7726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00572 0.08638 115.828 < 2e-16 ***
## category_code_LT01_4_count 0.78581 0.08883 8.846 < 2e-16 ***
## category_code_LT01_5_count 0.91873 0.06216 14.779 < 2e-16 ***
## category_code_LT01_7_count 0.36068 0.15822 2.280 0.02306 *
## category_code_LT01_8_count -0.14731 0.27226 -0.541 0.58872
## category_code_LT01_9_count 0.41017 0.22390 1.832 0.06757 .
## category_code_LT01_11_count 0.38023 0.11293 3.367 0.00082 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6325, Adjusted R-squared: 0.628
## F-statistic: 140.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count 0.620089807679342
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0276 -0.7865 -0.0086 0.8891 4.0382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00365 0.08732 114.570 < 2e-16 ***
## category_code_LT01_4_count 0.94095 0.07564 12.440 < 2e-16 ***
## category_code_LT01_5_count 0.92204 0.06303 14.628 < 2e-16 ***
## category_code_LT01_7_count 0.49222 0.15480 3.180 0.00157 **
## category_code_LT01_8_count -0.18530 0.27519 -0.673 0.50102
## category_code_LT01_9_count 0.46476 0.22563 2.060 0.03994 *
## category_code_LT01_12_count 0.18909 0.20499 0.922 0.35675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6247, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count 0.619493556456134
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0321 -0.7937 -0.0210 0.8467 4.0370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00489 0.08737 114.507 < 2e-16 ***
## category_code_LT01_4_count 0.95413 0.07433 12.836 < 2e-16 ***
## category_code_LT01_5_count 0.92674 0.06285 14.744 < 2e-16 ***
## category_code_LT01_7_count 0.48861 0.15621 3.128 0.00186 **
## category_code_LT01_8_count -0.17155 0.27572 -0.622 0.53410
## category_code_LT01_9_count 0.47364 0.22630 2.093 0.03686 *
## category_code_LT01_13_count 0.06987 0.24676 0.283 0.77717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6241, Adjusted R-squared: 0.6195
## F-statistic: 135.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count 0.619436866963043
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0328 -0.7950 -0.0280 0.8747 4.0364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00548 0.08746 114.398 < 2e-16 ***
## category_code_LT01_4_count 0.95574 0.07493 12.755 < 2e-16 ***
## category_code_LT01_5_count 0.92686 0.06309 14.691 < 2e-16 ***
## category_code_LT01_7_count 0.49348 0.15523 3.179 0.00157 **
## category_code_LT01_8_count -0.17648 0.27526 -0.641 0.52173
## category_code_LT01_9_count 0.46804 0.22620 2.069 0.03906 *
## category_code_LT01_14_count 0.02765 0.32986 0.084 0.93322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count 0.619675195326572
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0330 -0.7913 -0.0221 0.8659 4.0368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00506 0.08735 114.543 < 2e-16 ***
## category_code_LT01_4_count 0.94924 0.07493 12.669 < 2e-16 ***
## category_code_LT01_5_count 0.92777 0.06281 14.772 < 2e-16 ***
## category_code_LT01_7_count 0.49636 0.15492 3.204 0.00144 **
## category_code_LT01_8_count -0.17818 0.27518 -0.648 0.51760
## category_code_LT01_9_count 0.47082 0.22572 2.086 0.03751 *
## category_code_LT01_15_count 0.42131 0.75100 0.561 0.57506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6197
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_16_count 0.620225284064489
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0322 -0.7928 0.0105 0.8649 4.0365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00538 0.08728 114.629 < 2e-16 ***
## category_code_LT01_4_count 0.95278 0.07374 12.921 < 2e-16 ***
## category_code_LT01_5_count 0.92612 0.06277 14.754 < 2e-16 ***
## category_code_LT01_7_count 0.49477 0.15476 3.197 0.00148 **
## category_code_LT01_8_count -0.18982 0.27529 -0.690 0.49082
## category_code_LT01_9_count 0.45880 0.22577 2.032 0.04268 *
## category_code_LT01_16_count 1.18291 1.16762 1.013 0.31151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6248, Adjusted R-squared: 0.6202
## F-statistic: 136.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count 0.626465569559947
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0104 -0.7697 0.0399 0.8966 3.6481
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98372 0.08995 110.988 < 2e-16 ***
## category_code_LT01_4_count 0.79403 0.08888 8.934 < 2e-16 ***
## category_code_LT01_5_count 0.92588 0.06215 14.898 < 2e-16 ***
## category_code_LT01_7_count 0.37271 0.15837 2.353 0.018996 *
## category_code_LT01_8_count -0.13770 0.27275 -0.505 0.613874
## category_code_LT01_10_count 0.12723 0.11163 1.140 0.254951
## category_code_LT01_11_count 0.38995 0.11296 3.452 0.000604 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.631, Adjusted R-squared: 0.6265
## F-statistic: 139.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count 0.618030660011758
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0092 -0.7689 0.0147 0.8672 3.9070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97934 0.09096 109.715 < 2e-16 ***
## category_code_LT01_4_count 0.95590 0.07532 12.691 < 2e-16 ***
## category_code_LT01_5_count 0.93056 0.06305 14.759 < 2e-16 ***
## category_code_LT01_7_count 0.51021 0.15488 3.294 0.00106 **
## category_code_LT01_8_count -0.17488 0.27586 -0.634 0.52640
## category_code_LT01_10_count 0.14157 0.11288 1.254 0.21037
## category_code_LT01_12_count 0.18530 0.20575 0.901 0.36824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6226, Adjusted R-squared: 0.618
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count 0.61740890003356
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0132 -0.7693 0.0239 0.8485 3.9014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97975 0.09103 109.632 < 2e-16 ***
## category_code_LT01_4_count 0.97036 0.07391 13.129 < 2e-16 ***
## category_code_LT01_5_count 0.93562 0.06286 14.885 < 2e-16 ***
## category_code_LT01_7_count 0.50995 0.15610 3.267 0.00116 **
## category_code_LT01_8_count -0.16421 0.27645 -0.594 0.55279
## category_code_LT01_10_count 0.14634 0.11286 1.297 0.19536
## category_code_LT01_13_count 0.02685 0.24691 0.109 0.91346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count 0.617402792177685
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0130 -0.7687 0.0208 0.8552 3.8999
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97918 0.09149 109.077 < 2e-16 ***
## category_code_LT01_4_count 0.97206 0.07445 13.057 < 2e-16 ***
## category_code_LT01_5_count 0.93620 0.06312 14.832 < 2e-16 ***
## category_code_LT01_7_count 0.51249 0.15521 3.302 0.00103 **
## category_code_LT01_8_count -0.16594 0.27592 -0.601 0.54785
## category_code_LT01_10_count 0.14814 0.11539 1.284 0.19982
## category_code_LT01_14_count -0.02131 0.33762 -0.063 0.94970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.622, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count 0.617538745459179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0144 -0.7702 0.0199 0.8452 3.9047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98062 0.09104 109.633 < 2e-16 ***
## category_code_LT01_4_count 0.96598 0.07448 12.970 < 2e-16 ***
## category_code_LT01_5_count 0.93619 0.06282 14.902 < 2e-16 ***
## category_code_LT01_7_count 0.51412 0.15505 3.316 0.000981 ***
## category_code_LT01_8_count -0.16731 0.27588 -0.606 0.544482
## category_code_LT01_10_count 0.14249 0.11323 1.258 0.208832
## category_code_LT01_15_count 0.31936 0.75586 0.423 0.672833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6222, Adjusted R-squared: 0.6175
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_16_count 0.618241575647847
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0137 -0.7707 0.0252 0.8616 3.9061
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98113 0.09094 109.755 <2e-16 ***
## category_code_LT01_4_count 0.96699 0.07346 13.163 <2e-16 ***
## category_code_LT01_5_count 0.93438 0.06278 14.884 <2e-16 ***
## category_code_LT01_7_count 0.51234 0.15482 3.309 0.001 **
## category_code_LT01_8_count -0.18018 0.27594 -0.653 0.514
## category_code_LT01_10_count 0.14080 0.11285 1.248 0.213
## category_code_LT01_16_count 1.21840 1.17089 1.041 0.299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6229, Adjusted R-squared: 0.6182
## F-statistic: 135.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count 0.625477375162192
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0390 -0.7457 0.0346 0.9256 3.7551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.011873 0.086631 115.569 < 2e-16 ***
## category_code_LT01_4_count 0.803624 0.088652 9.065 < 2e-16 ***
## category_code_LT01_5_count 0.926611 0.062444 14.839 < 2e-16 ***
## category_code_LT01_7_count 0.385464 0.158447 2.433 0.015340 *
## category_code_LT01_8_count -0.130795 0.273334 -0.479 0.632494
## category_code_LT01_11_count 0.396666 0.117653 3.371 0.000807 ***
## category_code_LT01_12_count -0.001505 0.211924 -0.007 0.994337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count 0.625478574776581
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0389 -0.7455 0.0347 0.9261 3.7554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.011834 0.086616 115.589 < 2e-16 ***
## category_code_LT01_4_count 0.803314 0.088890 9.037 < 2e-16 ***
## category_code_LT01_5_count 0.926504 0.062251 14.883 < 2e-16 ***
## category_code_LT01_7_count 0.384822 0.159149 2.418 0.015969 *
## category_code_LT01_8_count -0.130211 0.273552 -0.476 0.634284
## category_code_LT01_11_count 0.396304 0.113013 3.507 0.000495 ***
## category_code_LT01_13_count 0.009843 0.244325 0.040 0.967880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count 0.625501713037439
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0389 -0.7394 0.0344 0.9122 3.7547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01252 0.08669 115.500 < 2e-16 ***
## category_code_LT01_4_count 0.80115 0.08965 8.936 < 2e-16 ***
## category_code_LT01_5_count 0.92550 0.06251 14.805 < 2e-16 ***
## category_code_LT01_7_count 0.38369 0.15851 2.421 0.015857 *
## category_code_LT01_8_count -0.13151 0.27305 -0.482 0.630293
## category_code_LT01_11_count 0.39624 0.11297 3.507 0.000494 ***
## category_code_LT01_14_count 0.05839 0.32662 0.179 0.858190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.63, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count 0.625534269146152
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0392 -0.7404 0.0337 0.9162 3.7569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01183 0.08661 115.601 < 2e-16 ***
## category_code_LT01_4_count 0.80103 0.08909 8.991 < 2e-16 ***
## category_code_LT01_5_count 0.92686 0.06223 14.894 < 2e-16 ***
## category_code_LT01_7_count 0.38744 0.15832 2.447 0.014747 *
## category_code_LT01_8_count -0.13197 0.27305 -0.483 0.629096
## category_code_LT01_11_count 0.39409 0.11329 3.479 0.000549 ***
## category_code_LT01_15_count 0.20417 0.74728 0.273 0.784798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.378 on 491 degrees of freedom
## Multiple R-squared: 0.6301, Adjusted R-squared: 0.6255
## F-statistic: 139.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_16_count 0.626185903267659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0382 -0.7351 0.0402 0.9149 3.7584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01194 0.08653 115.703 < 2e-16 ***
## category_code_LT01_4_count 0.80130 0.08855 9.049 < 2e-16 ***
## category_code_LT01_5_count 0.92534 0.06218 14.881 < 2e-16 ***
## category_code_LT01_7_count 0.38694 0.15803 2.448 0.014695 *
## category_code_LT01_8_count -0.14441 0.27314 -0.529 0.597250
## category_code_LT01_11_count 0.39173 0.11297 3.468 0.000571 ***
## category_code_LT01_16_count 1.11744 1.15830 0.965 0.335158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6262
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count 0.616816260406163
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0407 -0.7877 0.0083 0.8573 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01049 0.08763 114.238 < 2e-16 ***
## category_code_LT01_4_count 0.96787 0.07519 12.872 < 2e-16 ***
## category_code_LT01_5_count 0.93097 0.06317 14.738 < 2e-16 ***
## category_code_LT01_7_count 0.52487 0.15569 3.371 0.000807 ***
## category_code_LT01_8_count -0.16652 0.27680 -0.602 0.547718
## category_code_LT01_12_count 0.19744 0.20592 0.959 0.338126
## category_code_LT01_13_count 0.02701 0.24715 0.109 0.913018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count 0.616822069159869
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0408 -0.7880 0.0099 0.8639 4.0308
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01109 0.08771 114.139 < 2e-16 ***
## category_code_LT01_4_count 0.96694 0.07588 12.743 < 2e-16 ***
## category_code_LT01_5_count 0.93036 0.06340 14.674 < 2e-16 ***
## category_code_LT01_7_count 0.52545 0.15491 3.392 0.00075 ***
## category_code_LT01_8_count -0.16880 0.27626 -0.611 0.54146
## category_code_LT01_12_count 0.19597 0.20640 0.949 0.34284
## category_code_LT01_14_count 0.04613 0.33129 0.139 0.88932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617038531719492
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0411 -0.7934 0.0072 0.8635 4.0314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01048 0.08760 114.274 < 2e-16 ***
## category_code_LT01_4_count 0.96130 0.07596 12.655 < 2e-16 ***
## category_code_LT01_5_count 0.93155 0.06313 14.755 < 2e-16 ***
## category_code_LT01_7_count 0.52904 0.15455 3.423 0.000671 ***
## category_code_LT01_8_count -0.17034 0.27619 -0.617 0.537689
## category_code_LT01_12_count 0.19954 0.20578 0.970 0.332688
## category_code_LT01_15_count 0.41064 0.75360 0.545 0.586062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.617
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_16_count 0.617754688257407
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0398 -0.7936 0.0115 0.8641 4.0312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01064 0.08752 114.383 < 2e-16 ***
## category_code_LT01_4_count 0.96360 0.07480 12.883 < 2e-16 ***
## category_code_LT01_5_count 0.92958 0.06309 14.735 < 2e-16 ***
## category_code_LT01_7_count 0.52659 0.15436 3.412 0.000699 ***
## category_code_LT01_8_count -0.18368 0.27625 -0.665 0.506436
## category_code_LT01_12_count 0.19817 0.20557 0.964 0.335515
## category_code_LT01_16_count 1.29115 1.17020 1.103 0.270412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6224, Adjusted R-squared: 0.6178
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616133801044834
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0460 -0.7856 0.0132 0.8291 4.0290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01288 0.08777 114.083 < 2e-16 ***
## category_code_LT01_4_count 0.98147 0.07464 13.150 < 2e-16 ***
## category_code_LT01_5_count 0.93526 0.06325 14.786 < 2e-16 ***
## category_code_LT01_7_count 0.52451 0.15621 3.358 0.000847 ***
## category_code_LT01_8_count -0.15708 0.27686 -0.567 0.570717
## category_code_LT01_13_count 0.03458 0.24727 0.140 0.888845
## category_code_LT01_14_count 0.06996 0.33068 0.212 0.832543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6208, Adjusted R-squared: 0.6161
## F-statistic: 134 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count 0.616327613611308
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0463 -0.7578 0.0056 0.8328 4.0299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01201 0.08767 114.204 < 2e-16 ***
## category_code_LT01_4_count 0.97694 0.07453 13.108 < 2e-16 ***
## category_code_LT01_5_count 0.93694 0.06294 14.885 < 2e-16 ***
## category_code_LT01_7_count 0.52836 0.15580 3.391 0.000752 ***
## category_code_LT01_8_count -0.15767 0.27678 -0.570 0.569159
## category_code_LT01_13_count 0.04197 0.24762 0.170 0.865469
## category_code_LT01_15_count 0.40883 0.75554 0.541 0.588673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6163
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_16_count 0.61705432789913
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0451 -0.7621 0.0193 0.8365 4.0297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01216 0.08758 114.314 < 2e-16 ***
## category_code_LT01_4_count 0.97905 0.07330 13.357 < 2e-16 ***
## category_code_LT01_5_count 0.93492 0.06290 14.864 < 2e-16 ***
## category_code_LT01_7_count 0.52586 0.15563 3.379 0.000786 ***
## category_code_LT01_8_count -0.17112 0.27683 -0.618 0.536768
## category_code_LT01_13_count 0.04254 0.24708 0.172 0.863382
## category_code_LT01_16_count 1.29704 1.17183 1.107 0.268898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616337478202894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0465 -0.7726 -0.0033 0.8398 4.0290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01288 0.08774 114.117 < 2e-16 ***
## category_code_LT01_4_count 0.97567 0.07527 12.962 < 2e-16 ***
## category_code_LT01_5_count 0.93600 0.06321 14.807 < 2e-16 ***
## category_code_LT01_7_count 0.52932 0.15505 3.414 0.000694 ***
## category_code_LT01_8_count -0.16124 0.27628 -0.584 0.559736
## category_code_LT01_14_count 0.06723 0.33061 0.203 0.838935
## category_code_LT01_15_count 0.39929 0.75429 0.529 0.596793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.621, Adjusted R-squared: 0.6163
## F-statistic: 134.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617085961348876
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0453 -0.7827 0.0078 0.8577 4.0286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01326 0.08766 114.232 < 2e-16 ***
## category_code_LT01_4_count 0.97671 0.07421 13.161 < 2e-16 ***
## category_code_LT01_5_count 0.93361 0.06317 14.780 < 2e-16 ***
## category_code_LT01_7_count 0.52624 0.15485 3.398 0.000733 ***
## category_code_LT01_8_count -0.17509 0.27634 -0.634 0.526640
## category_code_LT01_14_count 0.08761 0.33065 0.265 0.791150
## category_code_LT01_16_count 1.30607 1.17263 1.114 0.265913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6217, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_8_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617278471201992
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0457 -0.7576 -0.0022 0.8577 4.0297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01221 0.08756 114.351 < 2e-16 ***
## category_code_LT01_4_count 0.97292 0.07400 13.148 < 2e-16 ***
## category_code_LT01_5_count 0.93568 0.06286 14.886 < 2e-16 ***
## category_code_LT01_7_count 0.53132 0.15448 3.439 0.000633 ***
## category_code_LT01_8_count -0.17611 0.27627 -0.637 0.524115
## category_code_LT01_15_count 0.42443 0.75358 0.563 0.573544
## category_code_LT01_16_count 1.30893 1.17137 1.117 0.264352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.628442757311434
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9998 -0.7668 0.0226 0.9217 3.6845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98175 0.08969 111.292 < 2e-16 ***
## category_code_LT01_4_count 0.77795 0.08906 8.735 < 2e-16 ***
## category_code_LT01_5_count 0.91351 0.06145 14.867 < 2e-16 ***
## category_code_LT01_7_count 0.34859 0.15834 2.202 0.028157 *
## category_code_LT01_9_count 0.38152 0.22524 1.694 0.090933 .
## category_code_LT01_10_count 0.10347 0.11210 0.923 0.356435
## category_code_LT01_11_count 0.37792 0.11290 3.347 0.000879 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6329, Adjusted R-squared: 0.6284
## F-statistic: 141.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.620522686296484
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9969 -0.7667 -0.0007 0.8976 3.9394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97713 0.09063 110.082 < 2e-16 ***
## category_code_LT01_4_count 0.93233 0.07609 12.253 < 2e-16 ***
## category_code_LT01_5_count 0.91591 0.06235 14.689 < 2e-16 ***
## category_code_LT01_7_count 0.47781 0.15508 3.081 0.00218 **
## category_code_LT01_9_count 0.43256 0.22707 1.905 0.05737 .
## category_code_LT01_10_count 0.11411 0.11332 1.007 0.31443
## category_code_LT01_12_count 0.17426 0.20498 0.850 0.39566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6251, Adjusted R-squared: 0.6205
## F-statistic: 136.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.620029021898737
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0006 -0.7671 -0.0126 0.8695 3.9349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97758 0.09069 110.017 < 2e-16 ***
## category_code_LT01_4_count 0.94393 0.07484 12.613 < 2e-16 ***
## category_code_LT01_5_count 0.92062 0.06212 14.821 < 2e-16 ***
## category_code_LT01_7_count 0.47376 0.15638 3.030 0.00258 **
## category_code_LT01_9_count 0.44063 0.22781 1.934 0.05367 .
## category_code_LT01_10_count 0.11774 0.11332 1.039 0.29930
## category_code_LT01_13_count 0.07132 0.24626 0.290 0.77224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.619979258358923
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0002 -0.7659 -0.0091 0.8741 3.9315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97631 0.09115 109.446 <2e-16 ***
## category_code_LT01_4_count 0.94829 0.07519 12.612 <2e-16 ***
## category_code_LT01_5_count 0.92191 0.06238 14.778 <2e-16 ***
## category_code_LT01_7_count 0.48045 0.15536 3.092 0.0021 **
## category_code_LT01_9_count 0.43692 0.22736 1.922 0.0552 .
## category_code_LT01_10_count 0.12204 0.11571 1.055 0.2921
## category_code_LT01_14_count -0.04710 0.33670 -0.140 0.8888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.62012935892787
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0020 -0.7673 -0.0135 0.8734 3.9380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97851 0.09070 110.015 < 2e-16 ***
## category_code_LT01_4_count 0.94072 0.07534 12.486 < 2e-16 ***
## category_code_LT01_5_count 0.92139 0.06209 14.839 < 2e-16 ***
## category_code_LT01_7_count 0.48154 0.15521 3.102 0.00203 **
## category_code_LT01_9_count 0.43817 0.22722 1.928 0.05438 .
## category_code_LT01_10_count 0.11408 0.11370 1.003 0.31616
## category_code_LT01_15_count 0.34821 0.75345 0.462 0.64417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6247, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.620639220916267
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0010 -0.7670 -0.0099 0.8810 3.9380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97872 0.09063 110.108 < 2e-16 ***
## category_code_LT01_4_count 0.94325 0.07428 12.698 < 2e-16 ***
## category_code_LT01_5_count 0.91954 0.06207 14.815 < 2e-16 ***
## category_code_LT01_7_count 0.48011 0.15504 3.097 0.00207 **
## category_code_LT01_9_count 0.42700 0.22720 1.879 0.06078 .
## category_code_LT01_10_count 0.11395 0.11329 1.006 0.31499
## category_code_LT01_16_count 1.09067 1.16679 0.935 0.35037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6252, Adjusted R-squared: 0.6206
## F-statistic: 136.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.627798738592179
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0227 -0.7420 0.0309 0.9091 3.7719
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.004368 0.086390 115.804 < 2e-16 ***
## category_code_LT01_4_count 0.784795 0.088889 8.829 < 2e-16 ***
## category_code_LT01_5_count 0.913934 0.061755 14.799 < 2e-16 ***
## category_code_LT01_7_count 0.357351 0.158421 2.256 0.02453 *
## category_code_LT01_9_count 0.406185 0.223845 1.815 0.07020 .
## category_code_LT01_11_count 0.383175 0.117513 3.261 0.00119 **
## category_code_LT01_12_count -0.006576 0.211039 -0.031 0.97515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6323, Adjusted R-squared: 0.6278
## F-statistic: 140.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.627829473707806
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0222 -0.7358 0.0313 0.9143 3.7734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00416 0.08637 115.828 < 2e-16 ***
## category_code_LT01_4_count 0.78312 0.08917 8.783 < 2e-16 ***
## category_code_LT01_5_count 0.91345 0.06152 14.849 < 2e-16 ***
## category_code_LT01_7_count 0.35393 0.15920 2.223 0.026661 *
## category_code_LT01_9_count 0.40954 0.22444 1.825 0.068654 .
## category_code_LT01_11_count 0.38134 0.11298 3.375 0.000796 ***
## category_code_LT01_13_count 0.04967 0.24376 0.204 0.838623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6323, Adjusted R-squared: 0.6278
## F-statistic: 140.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.627800942603021
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0225 -0.7388 0.0309 0.9121 3.7725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00455 0.08646 115.719 < 2e-16 ***
## category_code_LT01_4_count 0.78388 0.08980 8.730 < 2e-16 ***
## category_code_LT01_5_count 0.91340 0.06177 14.786 < 2e-16 ***
## category_code_LT01_7_count 0.35704 0.15846 2.253 0.02469 *
## category_code_LT01_9_count 0.40532 0.22426 1.807 0.07132 .
## category_code_LT01_11_count 0.38213 0.11291 3.384 0.00077 ***
## category_code_LT01_14_count 0.02032 0.32620 0.062 0.95037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6323, Adjusted R-squared: 0.6278
## F-statistic: 140.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.627866388810371
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0227 -0.7454 0.0233 0.9128 3.7745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00425 0.08636 115.839 < 2e-16 ***
## category_code_LT01_4_count 0.78181 0.08934 8.751 < 2e-16 ***
## category_code_LT01_5_count 0.91400 0.06150 14.862 < 2e-16 ***
## category_code_LT01_7_count 0.35962 0.15829 2.272 0.023527 *
## category_code_LT01_9_count 0.40741 0.22386 1.820 0.069381 .
## category_code_LT01_11_count 0.37955 0.11323 3.352 0.000864 ***
## category_code_LT01_15_count 0.22379 0.74500 0.300 0.764012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 491 degrees of freedom
## Multiple R-squared: 0.6324, Adjusted R-squared: 0.6279
## F-statistic: 140.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.628361343392394
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0217 -0.7300 0.0314 0.9113 3.7752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00439 0.08631 115.918 < 2e-16 ***
## category_code_LT01_4_count 0.78291 0.08879 8.818 < 2e-16 ***
## category_code_LT01_5_count 0.91242 0.06147 14.843 < 2e-16 ***
## category_code_LT01_7_count 0.35914 0.15806 2.272 0.023506 *
## category_code_LT01_9_count 0.39770 0.22389 1.776 0.076300 .
## category_code_LT01_11_count 0.37844 0.11291 3.352 0.000865 ***
## category_code_LT01_16_count 0.99601 1.15451 0.863 0.388717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.373 on 491 degrees of freedom
## Multiple R-squared: 0.6328, Adjusted R-squared: 0.6284
## F-statistic: 141.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.619806337235978
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0212 -0.7770 -0.0225 0.8722 4.0402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00171 0.08731 114.551 < 2e-16 ***
## category_code_LT01_4_count 0.93835 0.07622 12.312 < 2e-16 ***
## category_code_LT01_5_count 0.91557 0.06243 14.666 < 2e-16 ***
## category_code_LT01_7_count 0.48348 0.15605 3.098 0.00206 **
## category_code_LT01_9_count 0.46501 0.22620 2.056 0.04033 *
## category_code_LT01_12_count 0.18239 0.20502 0.890 0.37411
## category_code_LT01_13_count 0.07266 0.24632 0.295 0.76814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6244, Adjusted R-squared: 0.6198
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.61973905518008
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0217 -0.7783 -0.0266 0.8923 4.0399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.001962 0.087410 114.426 < 2e-16 ***
## category_code_LT01_4_count 0.940915 0.076658 12.274 < 2e-16 ***
## category_code_LT01_5_count 0.915925 0.062656 14.618 < 2e-16 ***
## category_code_LT01_7_count 0.489190 0.155117 3.154 0.00171 **
## category_code_LT01_9_count 0.460083 0.226045 2.035 0.04235 *
## category_code_LT01_12_count 0.183996 0.205512 0.895 0.37106
## category_code_LT01_14_count 0.003623 0.330608 0.011 0.99126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6243, Adjusted R-squared: 0.6197
## F-statistic: 136 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.619985109990246
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0218 -0.7740 -0.0256 0.8793 4.0401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00180 0.08729 114.582 <2e-16 ***
## category_code_LT01_4_count 0.93326 0.07690 12.136 <2e-16 ***
## category_code_LT01_5_count 0.91630 0.06240 14.684 <2e-16 ***
## category_code_LT01_7_count 0.49132 0.15481 3.174 0.0016 **
## category_code_LT01_9_count 0.46177 0.22558 2.047 0.0412 *
## category_code_LT01_12_count 0.18555 0.20490 0.906 0.3656
## category_code_LT01_15_count 0.42335 0.75069 0.564 0.5730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6246, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.620482180256941
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0207 -0.7753 -0.0246 0.8824 4.0399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00202 0.08723 114.660 < 2e-16 ***
## category_code_LT01_4_count 0.93709 0.07571 12.378 < 2e-16 ***
## category_code_LT01_5_count 0.91436 0.06238 14.658 < 2e-16 ***
## category_code_LT01_7_count 0.48954 0.15466 3.165 0.00165 **
## category_code_LT01_9_count 0.44985 0.22566 1.993 0.04676 *
## category_code_LT01_12_count 0.18406 0.20475 0.899 0.36912
## category_code_LT01_16_count 1.14320 1.16584 0.981 0.32728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6251, Adjusted R-squared: 0.6205
## F-statistic: 136.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.6191983486288
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0264 -0.7827 -0.0224 0.8515 4.0384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00352 0.08745 114.389 < 2e-16 ***
## category_code_LT01_4_count 0.95241 0.07558 12.601 < 2e-16 ***
## category_code_LT01_5_count 0.92048 0.06246 14.736 < 2e-16 ***
## category_code_LT01_7_count 0.48433 0.15648 3.095 0.00208 **
## category_code_LT01_9_count 0.46884 0.22673 2.068 0.03918 *
## category_code_LT01_13_count 0.07918 0.24642 0.321 0.74809
## category_code_LT01_14_count 0.02598 0.32995 0.079 0.93728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6238, Adjusted R-squared: 0.6192
## F-statistic: 135.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.619448123822932
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0265 -0.7830 -0.0305 0.8500 4.0388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00308 0.08734 114.534 < 2e-16 ***
## category_code_LT01_4_count 0.94533 0.07568 12.491 < 2e-16 ***
## category_code_LT01_5_count 0.92126 0.06217 14.819 < 2e-16 ***
## category_code_LT01_7_count 0.48650 0.15613 3.116 0.00194 **
## category_code_LT01_9_count 0.47210 0.22627 2.086 0.03745 *
## category_code_LT01_13_count 0.08762 0.24678 0.355 0.72271
## category_code_LT01_15_count 0.43130 0.75254 0.573 0.56682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.624, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.619953123584433
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0254 -0.7809 -0.0225 0.8489 4.0386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00329 0.08728 114.613 <2e-16 ***
## category_code_LT01_4_count 0.94917 0.07441 12.756 <2e-16 ***
## category_code_LT01_5_count 0.91925 0.06215 14.792 <2e-16 ***
## category_code_LT01_7_count 0.48475 0.15601 3.107 0.0020 **
## category_code_LT01_9_count 0.45992 0.22631 2.032 0.0427 *
## category_code_LT01_13_count 0.08655 0.24629 0.351 0.7254
## category_code_LT01_16_count 1.15626 1.16720 0.991 0.3224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.62
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.619354140670143
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0272 -0.7837 -0.0365 0.8687 4.0383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00362 0.08743 114.417 <2e-16 ***
## category_code_LT01_4_count 0.94813 0.07613 12.454 <2e-16 ***
## category_code_LT01_5_count 0.92140 0.06243 14.758 <2e-16 ***
## category_code_LT01_7_count 0.49278 0.15523 3.174 0.0016 **
## category_code_LT01_9_count 0.46536 0.22614 2.058 0.0401 *
## category_code_LT01_14_count 0.02284 0.32991 0.069 0.9448
## category_code_LT01_15_count 0.41443 0.75133 0.552 0.5815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.39 on 491 degrees of freedom
## Multiple R-squared: 0.6239, Adjusted R-squared: 0.6194
## F-statistic: 135.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.619870159146758
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0260 -0.7814 -0.0272 0.8694 4.0378
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00405 0.08737 114.499 < 2e-16 ***
## category_code_LT01_4_count 0.95085 0.07504 12.672 < 2e-16 ***
## category_code_LT01_5_count 0.91907 0.06242 14.724 < 2e-16 ***
## category_code_LT01_7_count 0.49045 0.15508 3.163 0.00166 **
## category_code_LT01_9_count 0.45254 0.22625 2.000 0.04603 *
## category_code_LT01_14_count 0.04214 0.33009 0.128 0.89847
## category_code_LT01_16_count 1.15137 1.16832 0.985 0.32487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 491 degrees of freedom
## Multiple R-squared: 0.6245, Adjusted R-squared: 0.6199
## F-statistic: 136.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.620117027524057
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0261 -0.7821 -0.0356 0.8623 4.0384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00345 0.08726 114.643 < 2e-16 ***
## category_code_LT01_4_count 0.94466 0.07501 12.594 < 2e-16 ***
## category_code_LT01_5_count 0.92018 0.06211 14.814 < 2e-16 ***
## category_code_LT01_7_count 0.49380 0.15476 3.191 0.00151 **
## category_code_LT01_9_count 0.45585 0.22573 2.019 0.04398 *
## category_code_LT01_15_count 0.43478 0.75076 0.579 0.56277
## category_code_LT01_16_count 1.16144 1.16680 0.995 0.32003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.388 on 491 degrees of freedom
## Multiple R-squared: 0.6247, Adjusted R-squared: 0.6201
## F-statistic: 136.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.626275636407859
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0064 -0.7640 0.0469 0.8959 3.6473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98269 0.08995 110.975 < 2e-16 ***
## category_code_LT01_4_count 0.79313 0.08892 8.920 < 2e-16 ***
## category_code_LT01_5_count 0.92158 0.06171 14.933 < 2e-16 ***
## category_code_LT01_7_count 0.36908 0.15858 2.327 0.020349 *
## category_code_LT01_10_count 0.12630 0.11171 1.131 0.258781
## category_code_LT01_11_count 0.39402 0.11751 3.353 0.000861 ***
## category_code_LT01_12_count -0.01531 0.21162 -0.072 0.942356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.626273343021669
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7664 0.0476 0.9004 3.6496
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98263 0.08995 110.980 < 2e-16 ***
## category_code_LT01_4_count 0.79260 0.08914 8.892 < 2e-16 ***
## category_code_LT01_5_count 0.92112 0.06147 14.985 < 2e-16 ***
## category_code_LT01_7_count 0.36896 0.15920 2.318 0.020885 *
## category_code_LT01_10_count 0.12589 0.11165 1.127 0.260088
## category_code_LT01_11_count 0.39152 0.11299 3.465 0.000576 ***
## category_code_LT01_13_count 0.01148 0.24366 0.047 0.962432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.626274887060119
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0057 -0.7688 0.0475 0.8993 3.6482
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98204 0.09040 110.421 < 2e-16 ***
## category_code_LT01_4_count 0.79372 0.08971 8.847 < 2e-16 ***
## category_code_LT01_5_count 0.92157 0.06176 14.922 < 2e-16 ***
## category_code_LT01_7_count 0.37029 0.15851 2.336 0.019892 *
## category_code_LT01_10_count 0.12755 0.11416 1.117 0.264437
## category_code_LT01_11_count 0.39167 0.11294 3.468 0.000571 ***
## category_code_LT01_14_count -0.02175 0.33367 -0.065 0.948051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.626294554492861
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0065 -0.7649 0.0469 0.9032 3.6518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98296 0.08997 110.962 < 2e-16 ***
## category_code_LT01_4_count 0.79140 0.08930 8.862 < 2e-16 ***
## category_code_LT01_5_count 0.92134 0.06147 14.990 < 2e-16 ***
## category_code_LT01_7_count 0.37112 0.15849 2.342 0.019603 *
## category_code_LT01_10_count 0.12438 0.11201 1.110 0.267373
## category_code_LT01_11_count 0.39027 0.11323 3.447 0.000616 ***
## category_code_LT01_15_count 0.12993 0.74903 0.173 0.862358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6308, Adjusted R-squared: 0.6263
## F-statistic: 139.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.626868037313947
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7579 0.0486 0.9084 3.6563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98364 0.08989 111.071 < 2e-16 ***
## category_code_LT01_4_count 0.79106 0.08883 8.905 < 2e-16 ***
## category_code_LT01_5_count 0.91965 0.06143 14.970 < 2e-16 ***
## category_code_LT01_7_count 0.37127 0.15818 2.347 0.019320 *
## category_code_LT01_10_count 0.12119 0.11167 1.085 0.278367
## category_code_LT01_11_count 0.38775 0.11294 3.433 0.000647 ***
## category_code_LT01_16_count 1.02503 1.15708 0.886 0.376118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.376 on 491 degrees of freedom
## Multiple R-squared: 0.6314, Adjusted R-squared: 0.6269
## F-statistic: 140.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.617729751744807
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0037 -0.7652 0.0067 0.8711 3.9102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97794 0.09097 109.689 < 2e-16 ***
## category_code_LT01_4_count 0.95489 0.07577 12.602 < 2e-16 ***
## category_code_LT01_5_count 0.92463 0.06242 14.814 < 2e-16 ***
## category_code_LT01_7_count 0.50505 0.15594 3.239 0.00128 **
## category_code_LT01_10_count 0.13996 0.11292 1.239 0.21579
## category_code_LT01_12_count 0.18001 0.20578 0.875 0.38212
## category_code_LT01_13_count 0.03027 0.24641 0.123 0.90229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.617730415750636
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0032 -0.7657 -0.0018 0.8806 3.9077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97678 0.09143 109.124 < 2e-16 ***
## category_code_LT01_4_count 0.95728 0.07618 12.566 < 2e-16 ***
## category_code_LT01_5_count 0.92549 0.06267 14.767 < 2e-16 ***
## category_code_LT01_7_count 0.50831 0.15508 3.278 0.00112 **
## category_code_LT01_10_count 0.14325 0.11540 1.241 0.21507
## category_code_LT01_12_count 0.18240 0.20615 0.885 0.37672
## category_code_LT01_14_count -0.04270 0.33819 -0.126 0.89958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.617862650164984
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0049 -0.7674 -0.0001 0.8730 3.9137
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97880 0.09097 109.691 < 2e-16 ***
## category_code_LT01_4_count 0.95033 0.07643 12.435 < 2e-16 ***
## category_code_LT01_5_count 0.92506 0.06240 14.824 < 2e-16 ***
## category_code_LT01_7_count 0.50944 0.15492 3.288 0.00108 **
## category_code_LT01_10_count 0.13598 0.11331 1.200 0.23070
## category_code_LT01_12_count 0.18217 0.20570 0.886 0.37626
## category_code_LT01_15_count 0.32574 0.75560 0.431 0.66658
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 134.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.618512122884445
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0037 -0.7655 0.0189 0.8832 3.9150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97915 0.09088 109.805 < 2e-16 ***
## category_code_LT01_4_count 0.95165 0.07539 12.623 < 2e-16 ***
## category_code_LT01_5_count 0.92290 0.06237 14.797 < 2e-16 ***
## category_code_LT01_7_count 0.50741 0.15471 3.280 0.00111 **
## category_code_LT01_10_count 0.13447 0.11293 1.191 0.23434
## category_code_LT01_12_count 0.18089 0.20549 0.880 0.37914
## category_code_LT01_16_count 1.18190 1.16907 1.011 0.31252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.391 on 491 degrees of freedom
## Multiple R-squared: 0.6231, Adjusted R-squared: 0.6185
## F-statistic: 135.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.617137364570649
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7665 0.0230 0.8683 3.9031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97781 0.09149 109.059 < 2e-16 ***
## category_code_LT01_4_count 0.97047 0.07497 12.945 < 2e-16 ***
## category_code_LT01_5_count 0.93041 0.06247 14.895 < 2e-16 ***
## category_code_LT01_7_count 0.50704 0.15628 3.244 0.00126 **
## category_code_LT01_10_count 0.14648 0.11545 1.269 0.20512
## category_code_LT01_13_count 0.03576 0.24654 0.145 0.88472
## category_code_LT01_14_count -0.02229 0.33776 -0.066 0.94740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6218, Adjusted R-squared: 0.6171
## F-statistic: 134.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.617275287662025
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0090 -0.7688 -0.0128 0.8541 3.9081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97928 0.09104 109.614 < 2e-16 ***
## category_code_LT01_4_count 0.96407 0.07507 12.843 < 2e-16 ***
## category_code_LT01_5_count 0.93031 0.06216 14.967 < 2e-16 ***
## category_code_LT01_7_count 0.50814 0.15607 3.256 0.00121 **
## category_code_LT01_10_count 0.14064 0.11329 1.241 0.21505
## category_code_LT01_13_count 0.04246 0.24694 0.172 0.86356
## category_code_LT01_15_count 0.32254 0.75750 0.426 0.67044
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617935717756749
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0078 -0.7692 0.0120 0.8700 3.9095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97964 0.09095 109.730 < 2e-16 ***
## category_code_LT01_4_count 0.96513 0.07399 13.045 < 2e-16 ***
## category_code_LT01_5_count 0.92809 0.06213 14.939 < 2e-16 ***
## category_code_LT01_7_count 0.50596 0.15589 3.246 0.00125 **
## category_code_LT01_10_count 0.13900 0.11290 1.231 0.21886
## category_code_LT01_13_count 0.04474 0.24641 0.182 0.85599
## category_code_LT01_16_count 1.18830 1.17066 1.015 0.31058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.617255649001162
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7682 -0.0154 0.8530 3.9064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97865 0.09150 109.057 < 2e-16 ***
## category_code_LT01_4_count 0.96647 0.07552 12.797 < 2e-16 ***
## category_code_LT01_5_count 0.93092 0.06245 14.907 < 2e-16 ***
## category_code_LT01_7_count 0.51178 0.15525 3.297 0.00105 **
## category_code_LT01_10_count 0.14276 0.11582 1.233 0.21831
## category_code_LT01_14_count -0.02232 0.33767 -0.066 0.94733
## category_code_LT01_15_count 0.31430 0.75610 0.416 0.67783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6219, Adjusted R-squared: 0.6173
## F-statistic: 134.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617910103266594
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7691 0.0108 0.8699 3.9089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97956 0.09141 109.168 < 2e-16 ***
## category_code_LT01_4_count 0.96678 0.07456 12.967 < 2e-16 ***
## category_code_LT01_5_count 0.92837 0.06243 14.871 < 2e-16 ***
## category_code_LT01_7_count 0.50934 0.15504 3.285 0.00109 **
## category_code_LT01_10_count 0.13965 0.11552 1.209 0.22730
## category_code_LT01_14_count -0.00245 0.33800 -0.007 0.99422
## category_code_LT01_16_count 1.18035 1.17213 1.007 0.31442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6225, Adjusted R-squared: 0.6179
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.618066385382067
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0091 -0.7701 -0.0107 0.8610 3.9130
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98055 0.09095 109.731 < 2e-16 ***
## category_code_LT01_4_count 0.96100 0.07456 12.889 < 2e-16 ***
## category_code_LT01_5_count 0.92865 0.06211 14.953 < 2e-16 ***
## category_code_LT01_7_count 0.51154 0.15487 3.303 0.00103 **
## category_code_LT01_10_count 0.13501 0.11329 1.192 0.23396
## category_code_LT01_15_count 0.33876 0.75566 0.448 0.65414
## category_code_LT01_16_count 1.19730 1.17032 1.023 0.30679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.392 on 491 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6181
## F-statistic: 135 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.62530646550512
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.7431 0.0443 0.9298 3.7549
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.010578 0.086612 115.580 < 2e-16 ***
## category_code_LT01_4_count 0.802061 0.088916 9.020 < 2e-16 ***
## category_code_LT01_5_count 0.922180 0.061800 14.922 < 2e-16 ***
## category_code_LT01_7_count 0.381126 0.159296 2.393 0.017106 *
## category_code_LT01_11_count 0.398773 0.117614 3.391 0.000754 ***
## category_code_LT01_12_count -0.006499 0.211787 -0.031 0.975531
## category_code_LT01_13_count 0.017103 0.243974 0.070 0.944140
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.625326166462659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0347 -0.7456 0.0477 0.9182 3.7539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.011273 0.086690 115.483 < 2e-16 ***
## category_code_LT01_4_count 0.800176 0.089669 8.924 < 2e-16 ***
## category_code_LT01_5_count 0.921254 0.062052 14.847 < 2e-16 ***
## category_code_LT01_7_count 0.380397 0.158733 2.396 0.016927 *
## category_code_LT01_11_count 0.399211 0.117587 3.395 0.000742 ***
## category_code_LT01_12_count -0.009001 0.212340 -0.042 0.966207
## category_code_LT01_14_count 0.057430 0.327597 0.175 0.860911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.625356432043919
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0348 -0.7419 0.0433 0.9250 3.7566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.010554 0.086605 115.589 < 2e-16 ***
## category_code_LT01_4_count 0.800012 0.089152 8.974 < 2e-16 ***
## category_code_LT01_5_count 0.922440 0.061791 14.928 < 2e-16 ***
## category_code_LT01_7_count 0.384258 0.158503 2.424 0.01570 *
## category_code_LT01_11_count 0.396395 0.117972 3.360 0.00084 ***
## category_code_LT01_12_count -0.004248 0.211859 -0.020 0.98401
## category_code_LT01_15_count 0.198423 0.747834 0.265 0.79087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.625973379734815
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0335 -0.7373 0.0534 0.9217 3.7581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.010535 0.086533 115.684 < 2e-16 ***
## category_code_LT01_4_count 0.800164 0.088601 9.031 < 2e-16 ***
## category_code_LT01_5_count 0.920548 0.061763 14.905 < 2e-16 ***
## category_code_LT01_7_count 0.383511 0.158213 2.424 0.015710 *
## category_code_LT01_11_count 0.394232 0.117585 3.353 0.000862 ***
## category_code_LT01_12_count -0.004125 0.211568 -0.019 0.984453
## category_code_LT01_16_count 1.085769 1.157165 0.938 0.348551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.625328667167171
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0344 -0.7449 0.0482 0.9208 3.7551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01115 0.08667 115.515 < 2e-16 ***
## category_code_LT01_4_count 0.79958 0.08993 8.891 < 2e-16 ***
## category_code_LT01_5_count 0.92095 0.06185 14.891 < 2e-16 ***
## category_code_LT01_7_count 0.37958 0.15937 2.382 0.017611 *
## category_code_LT01_11_count 0.39758 0.11300 3.518 0.000474 ***
## category_code_LT01_13_count 0.01738 0.24393 0.071 0.943243
## category_code_LT01_14_count 0.05662 0.32669 0.173 0.862478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.625361784459626
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7403 0.0441 0.9268 3.7573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01047 0.08659 115.613 < 2e-16 ***
## category_code_LT01_4_count 0.79929 0.08941 8.940 < 2e-16 ***
## category_code_LT01_5_count 0.92223 0.06155 14.985 < 2e-16 ***
## category_code_LT01_7_count 0.38300 0.15912 2.407 0.016454 *
## category_code_LT01_11_count 0.39539 0.11333 3.489 0.000529 ***
## category_code_LT01_13_count 0.02105 0.24437 0.086 0.931405
## category_code_LT01_15_count 0.20293 0.74881 0.271 0.786502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.625981106978873
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0332 -0.7361 0.0541 0.9224 3.7589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01045 0.08651 115.709 < 2e-16 ***
## category_code_LT01_4_count 0.79937 0.08883 8.999 < 2e-16 ***
## category_code_LT01_5_count 0.92031 0.06152 14.961 < 2e-16 ***
## category_code_LT01_7_count 0.38193 0.15888 2.404 0.016593 *
## category_code_LT01_11_count 0.39323 0.11300 3.480 0.000546 ***
## category_code_LT01_13_count 0.02502 0.24386 0.103 0.918332
## category_code_LT01_16_count 1.09018 1.15780 0.942 0.346866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.625377983535622
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0346 -0.7448 0.0454 0.9145 3.7566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01113 0.08666 115.524 < 2e-16 ***
## category_code_LT01_4_count 0.79764 0.09012 8.851 < 2e-16 ***
## category_code_LT01_5_count 0.92129 0.06184 14.899 < 2e-16 ***
## category_code_LT01_7_count 0.38267 0.15858 2.413 0.016182 *
## category_code_LT01_11_count 0.39557 0.11326 3.493 0.000522 ***
## category_code_LT01_14_count 0.05529 0.32667 0.169 0.865663
## category_code_LT01_15_count 0.19734 0.74742 0.264 0.791868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6299, Adjusted R-squared: 0.6254
## F-statistic: 139.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.626009566357366
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0332 -0.7422 0.0543 0.8906 3.7580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01129 0.08659 115.623 < 2e-16 ***
## category_code_LT01_4_count 0.79707 0.08962 8.894 < 2e-16 ***
## category_code_LT01_5_count 0.91908 0.06181 14.869 < 2e-16 ***
## category_code_LT01_7_count 0.38142 0.15829 2.410 0.016335 *
## category_code_LT01_11_count 0.39331 0.11295 3.482 0.000541 ***
## category_code_LT01_14_count 0.07151 0.32676 0.219 0.826870
## category_code_LT01_16_count 1.09838 1.15843 0.948 0.343513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.626039268833578
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0335 -0.7352 0.0479 0.9018 3.7604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01045 0.08651 115.720 < 2e-16 ***
## category_code_LT01_4_count 0.79730 0.08905 8.954 < 2e-16 ***
## category_code_LT01_5_count 0.92069 0.06150 14.970 < 2e-16 ***
## category_code_LT01_7_count 0.38573 0.15810 2.440 0.015047 *
## category_code_LT01_11_count 0.39103 0.11327 3.452 0.000604 ***
## category_code_LT01_15_count 0.22021 0.74704 0.295 0.768292
## category_code_LT01_16_count 1.09626 1.15752 0.947 0.344067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 491 degrees of freedom
## Multiple R-squared: 0.6306, Adjusted R-squared: 0.626
## F-statistic: 139.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.616548037956972
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0351 -0.7833 0.0230 0.8604 4.0325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00936 0.08770 114.133 < 2e-16 ***
## category_code_LT01_4_count 0.96562 0.07636 12.645 < 2e-16 ***
## category_code_LT01_5_count 0.92462 0.06278 14.729 < 2e-16 ***
## category_code_LT01_7_count 0.51975 0.15601 3.331 0.000929 ***
## category_code_LT01_12_count 0.19061 0.20642 0.923 0.356248
## category_code_LT01_13_count 0.03677 0.24676 0.149 0.881592
## category_code_LT01_14_count 0.04473 0.33141 0.135 0.892689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6165
## F-statistic: 134.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.61676699853878
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0353 -0.7701 0.0168 0.8494 4.0331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00874 0.08759 114.266 < 2e-16 ***
## category_code_LT01_4_count 0.95964 0.07652 12.540 < 2e-16 ***
## category_code_LT01_5_count 0.92571 0.06250 14.813 < 2e-16 ***
## category_code_LT01_7_count 0.52272 0.15561 3.359 0.000843 ***
## category_code_LT01_12_count 0.19391 0.20579 0.942 0.346526
## category_code_LT01_13_count 0.04435 0.24710 0.179 0.857629
## category_code_LT01_15_count 0.41274 0.75512 0.547 0.584908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.617436912459892
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0337 -0.7775 0.0263 0.8521 4.0331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00877 0.08751 114.366 < 2e-16 ***
## category_code_LT01_4_count 0.96206 0.07529 12.778 < 2e-16 ***
## category_code_LT01_5_count 0.92334 0.06247 14.781 < 2e-16 ***
## category_code_LT01_7_count 0.51996 0.15545 3.345 0.000886 ***
## category_code_LT01_12_count 0.19216 0.20560 0.935 0.350445
## category_code_LT01_13_count 0.04537 0.24660 0.184 0.854088
## category_code_LT01_16_count 1.25951 1.16989 1.077 0.282184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616754164912461
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0355 -0.7786 0.0173 0.8729 4.0326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00930 0.08768 114.164 < 2e-16 ***
## category_code_LT01_4_count 0.95964 0.07708 12.450 < 2e-16 ***
## category_code_LT01_5_count 0.92517 0.06275 14.743 < 2e-16 ***
## category_code_LT01_7_count 0.52466 0.15490 3.387 0.000763 ***
## category_code_LT01_12_count 0.19296 0.20630 0.935 0.350079
## category_code_LT01_14_count 0.04161 0.33133 0.126 0.900108
## category_code_LT01_15_count 0.40339 0.75389 0.535 0.592841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.617437392960582
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0338 -0.7908 0.0277 0.8769 4.0323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00956 0.08760 114.269 < 2e-16 ***
## category_code_LT01_4_count 0.96112 0.07601 12.645 < 2e-16 ***
## category_code_LT01_5_count 0.92246 0.06273 14.705 < 2e-16 ***
## category_code_LT01_7_count 0.52138 0.15472 3.370 0.000811 ***
## category_code_LT01_12_count 0.19034 0.20610 0.924 0.356199
## category_code_LT01_14_count 0.06153 0.33140 0.186 0.852780
## category_code_LT01_16_count 1.26279 1.17060 1.079 0.281228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6221, Adjusted R-squared: 0.6174
## F-statistic: 134.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617660395266191
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0340 -0.7681 0.0226 0.8645 4.0331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00875 0.08749 114.401 < 2e-16 ***
## category_code_LT01_4_count 0.95576 0.07607 12.564 < 2e-16 ***
## category_code_LT01_5_count 0.92387 0.06244 14.796 < 2e-16 ***
## category_code_LT01_7_count 0.52552 0.15435 3.405 0.000716 ***
## category_code_LT01_12_count 0.19463 0.20548 0.947 0.343988
## category_code_LT01_15_count 0.42665 0.75320 0.566 0.571342
## category_code_LT01_16_count 1.26996 1.16930 1.086 0.277972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.393 on 491 degrees of freedom
## Multiple R-squared: 0.6223, Adjusted R-squared: 0.6177
## F-statistic: 134.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.616104667517727
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0409 -0.7556 0.0027 0.8385 4.0307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01115 0.08773 114.118 < 2e-16 ***
## category_code_LT01_4_count 0.97336 0.07590 12.824 < 2e-16 ***
## category_code_LT01_5_count 0.93030 0.06255 14.872 < 2e-16 ***
## category_code_LT01_7_count 0.52265 0.15612 3.348 0.000878 ***
## category_code_LT01_13_count 0.05106 0.24724 0.207 0.836460
## category_code_LT01_14_count 0.06547 0.33069 0.198 0.843148
## category_code_LT01_15_count 0.40322 0.75579 0.534 0.593923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 491 degrees of freedom
## Multiple R-squared: 0.6207, Adjusted R-squared: 0.6161
## F-statistic: 133.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.616808200980307
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0391 -0.7811 0.0054 0.8384 4.0305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01138 0.08765 114.226 < 2e-16 ***
## category_code_LT01_4_count 0.97451 0.07478 13.032 < 2e-16 ***
## category_code_LT01_5_count 0.92748 0.06253 14.832 < 2e-16 ***
## category_code_LT01_7_count 0.51922 0.15596 3.329 0.000937 ***
## category_code_LT01_13_count 0.05248 0.24672 0.213 0.831643
## category_code_LT01_14_count 0.08528 0.33076 0.258 0.796638
## category_code_LT01_16_count 1.27695 1.17224 1.089 0.276547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6214, Adjusted R-squared: 0.6168
## F-statistic: 134.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617008048898672
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0394 -0.7483 0.0068 0.8316 4.0316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01033 0.08755 114.344 < 2e-16 ***
## category_code_LT01_4_count 0.97026 0.07465 12.997 < 2e-16 ***
## category_code_LT01_5_count 0.92944 0.06220 14.943 < 2e-16 ***
## category_code_LT01_7_count 0.52364 0.15555 3.366 0.000821 ***
## category_code_LT01_13_count 0.06022 0.24709 0.244 0.807552
## category_code_LT01_15_count 0.42899 0.75516 0.568 0.570235
## category_code_LT01_16_count 1.28143 1.17103 1.094 0.274372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_7_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.617010015611792
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0396 -0.7555 0.0075 0.8577 4.0305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01137 0.08762 114.257 < 2e-16 ***
## category_code_LT01_4_count 0.96902 0.07542 12.848 < 2e-16 ***
## category_code_LT01_5_count 0.92818 0.06250 14.850 < 2e-16 ***
## category_code_LT01_7_count 0.52540 0.15484 3.393 0.000747 ***
## category_code_LT01_14_count 0.08228 0.33067 0.249 0.803591
## category_code_LT01_15_count 0.41563 0.75383 0.551 0.581637
## category_code_LT01_16_count 1.28530 1.17164 1.097 0.273175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 491 degrees of freedom
## Multiple R-squared: 0.6216, Adjusted R-squared: 0.617
## F-statistic: 134.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count 0.62495067851954
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0048 -0.7670 0.0369 0.9165 3.6268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97983 0.09013 110.733 < 2e-16 ***
## category_code_LT01_4_count 0.81752 0.08778 9.313 < 2e-16 ***
## category_code_LT01_5_count 0.92351 0.06238 14.805 < 2e-16 ***
## category_code_LT01_8_count -0.13113 0.27326 -0.480 0.6315
## category_code_LT01_9_count 0.42426 0.22572 1.880 0.0608 .
## category_code_LT01_10_count 0.11983 0.11243 1.066 0.2870
## category_code_LT01_11_count 0.43797 0.10996 3.983 7.83e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6295, Adjusted R-squared: 0.625
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count 0.613469441700857
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0034 -0.8003 -0.0025 0.9252 3.9139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97314 0.09148 109.018 <2e-16 ***
## category_code_LT01_4_count 1.02798 0.07016 14.652 <2e-16 ***
## category_code_LT01_5_count 0.93105 0.06351 14.659 <2e-16 ***
## category_code_LT01_8_count -0.16663 0.27750 -0.600 0.5485
## category_code_LT01_9_count 0.50656 0.22813 2.220 0.0268 *
## category_code_LT01_10_count 0.14098 0.11408 1.236 0.2171
## category_code_LT01_12_count 0.18621 0.20699 0.900 0.3688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 491 degrees of freedom
## Multiple R-squared: 0.6181, Adjusted R-squared: 0.6135
## F-statistic: 132.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count 0.613149093328045
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0064 -0.7779 -0.0118 0.9057 3.9112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97359 0.09152 108.979 <2e-16 ***
## category_code_LT01_4_count 1.03510 0.06932 14.933 <2e-16 ***
## category_code_LT01_5_count 0.93471 0.06332 14.762 <2e-16 ***
## category_code_LT01_8_count -0.14785 0.27789 -0.532 0.5949
## category_code_LT01_9_count 0.51865 0.22861 2.269 0.0237 *
## category_code_LT01_10_count 0.14300 0.11408 1.254 0.2106
## category_code_LT01_13_count 0.15658 0.24695 0.634 0.5264
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6178, Adjusted R-squared: 0.6131
## F-statistic: 132.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count 0.612832580478225
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0075 -0.7845 -0.0057 0.9059 3.9083
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.973688 0.092025 108.380 <2e-16 ***
## category_code_LT01_4_count 1.043345 0.069356 15.043 <2e-16 ***
## category_code_LT01_5_count 0.936205 0.063586 14.723 <2e-16 ***
## category_code_LT01_8_count -0.157869 0.277570 -0.569 0.5698
## category_code_LT01_9_count 0.509810 0.228492 2.231 0.0261 *
## category_code_LT01_10_count 0.145531 0.116580 1.248 0.2125
## category_code_LT01_14_count 0.005682 0.339454 0.017 0.9867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.6128
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count 0.612941254807507
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0084 -0.7891 0.0008 0.9045 3.9113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97427 0.09156 108.932 <2e-16 ***
## category_code_LT01_4_count 1.03912 0.06908 15.043 <2e-16 ***
## category_code_LT01_5_count 0.93663 0.06329 14.798 <2e-16 ***
## category_code_LT01_8_count -0.15890 0.27754 -0.573 0.5672
## category_code_LT01_9_count 0.51223 0.22834 2.243 0.0253 *
## category_code_LT01_10_count 0.14226 0.11445 1.243 0.2144
## category_code_LT01_15_count 0.28256 0.76025 0.372 0.7103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6176, Adjusted R-squared: 0.6129
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_16_count 0.613526717865031
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0079 -0.7882 -0.0119 0.9203 3.9120
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97478 0.09148 109.034 <2e-16 ***
## category_code_LT01_4_count 1.04033 0.06807 15.283 <2e-16 ***
## category_code_LT01_5_count 0.93518 0.06325 14.785 <2e-16 ***
## category_code_LT01_8_count -0.17030 0.27763 -0.613 0.5399
## category_code_LT01_9_count 0.50153 0.22826 2.197 0.0285 *
## category_code_LT01_10_count 0.14121 0.11405 1.238 0.2162
## category_code_LT01_16_count 1.10737 1.17901 0.939 0.3481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 491 degrees of freedom
## Multiple R-squared: 0.6182, Adjusted R-squared: 0.6135
## F-statistic: 132.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count 0.624097529865557
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0318 -0.7614 0.0246 0.9368 3.7244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00614 0.08686 115.204 < 2e-16 ***
## category_code_LT01_4_count 0.82675 0.08750 9.448 < 2e-16 ***
## category_code_LT01_5_count 0.92446 0.06266 14.754 < 2e-16 ***
## category_code_LT01_8_count -0.12355 0.27379 -0.451 0.6520
## category_code_LT01_9_count 0.45376 0.22425 2.023 0.0436 *
## category_code_LT01_11_count 0.44910 0.11425 3.931 9.68e-05 ***
## category_code_LT01_12_count -0.02917 0.21196 -0.138 0.8906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count 0.624227137364267
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0300 -0.7621 0.0387 0.9151 3.7302
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00551 0.08683 115.233 < 2e-16 ***
## category_code_LT01_4_count 0.82216 0.08803 9.340 < 2e-16 ***
## category_code_LT01_5_count 0.92277 0.06248 14.769 < 2e-16 ***
## category_code_LT01_8_count -0.11874 0.27388 -0.434 0.6648
## category_code_LT01_9_count 0.45993 0.22464 2.047 0.0411 *
## category_code_LT01_11_count 0.44171 0.11011 4.012 6.97e-05 ***
## category_code_LT01_13_count 0.10575 0.24370 0.434 0.6645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6288, Adjusted R-squared: 0.6242
## F-statistic: 138.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count 0.624114619720693
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0310 -0.7641 0.0370 0.9412 3.7272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00669 0.08692 115.122 < 2e-16 ***
## category_code_LT01_4_count 0.82358 0.08862 9.293 < 2e-16 ***
## category_code_LT01_5_count 0.92257 0.06272 14.708 < 2e-16 ***
## category_code_LT01_8_count -0.12593 0.27353 -0.460 0.6455
## category_code_LT01_9_count 0.45091 0.22473 2.006 0.0454 *
## category_code_LT01_11_count 0.44434 0.10992 4.043 6.14e-05 ***
## category_code_LT01_14_count 0.06648 0.32725 0.203 0.8391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count 0.624116976682731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0312 -0.7669 0.0374 0.9284 3.7288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00587 0.08684 115.227 < 2e-16 ***
## category_code_LT01_4_count 0.82461 0.08792 9.379 < 2e-16 ***
## category_code_LT01_5_count 0.92398 0.06246 14.794 < 2e-16 ***
## category_code_LT01_8_count -0.12599 0.27354 -0.461 0.645
## category_code_LT01_9_count 0.45499 0.22430 2.029 0.043 *
## category_code_LT01_11_count 0.44321 0.11015 4.024 6.64e-05 ***
## category_code_LT01_15_count 0.15755 0.74816 0.211 0.833
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_16_count 0.624644671495453
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0306 -0.7652 0.0393 0.9336 3.7299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00610 0.08678 115.310 < 2e-16 ***
## category_code_LT01_4_count 0.82497 0.08743 9.435 < 2e-16 ***
## category_code_LT01_5_count 0.92284 0.06241 14.786 < 2e-16 ***
## category_code_LT01_8_count -0.13683 0.27365 -0.500 0.6173
## category_code_LT01_9_count 0.44599 0.22428 1.989 0.0473 *
## category_code_LT01_11_count 0.44125 0.10989 4.015 6.87e-05 ***
## category_code_LT01_16_count 0.99567 1.16163 0.857 0.3918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count 0.612604148317627
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0324 -0.7927 0.0203 0.9347 4.0387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00315 0.08818 113.446 <2e-16 ***
## category_code_LT01_4_count 1.03297 0.07062 14.628 <2e-16 ***
## category_code_LT01_5_count 0.92970 0.06363 14.612 <2e-16 ***
## category_code_LT01_8_count -0.15037 0.27823 -0.540 0.5891
## category_code_LT01_9_count 0.55112 0.22684 2.430 0.0155 *
## category_code_LT01_12_count 0.19409 0.20709 0.937 0.3491
## category_code_LT01_13_count 0.16145 0.24706 0.654 0.5137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6173, Adjusted R-squared: 0.6126
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count 0.61230253544549
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0341 -0.7941 0.0138 0.9405 4.0373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00457 0.08830 113.303 <2e-16 ***
## category_code_LT01_4_count 1.03861 0.07085 14.658 <2e-16 ***
## category_code_LT01_5_count 0.93005 0.06386 14.565 <2e-16 ***
## category_code_LT01_8_count -0.16131 0.27790 -0.580 0.5619
## category_code_LT01_9_count 0.53963 0.22709 2.376 0.0179 *
## category_code_LT01_12_count 0.19532 0.20763 0.941 0.3473
## category_code_LT01_14_count 0.07050 0.33319 0.212 0.8325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.617, Adjusted R-squared: 0.6123
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count 0.612458913331242
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0342 -0.7994 0.0154 0.9323 4.0383
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00362 0.08819 113.435 <2e-16 ***
## category_code_LT01_4_count 1.03514 0.07062 14.658 <2e-16 ***
## category_code_LT01_5_count 0.93161 0.06360 14.648 <2e-16 ***
## category_code_LT01_8_count -0.16244 0.27785 -0.585 0.5591
## category_code_LT01_9_count 0.54454 0.22656 2.404 0.0166 *
## category_code_LT01_12_count 0.19988 0.20703 0.965 0.3348
## category_code_LT01_15_count 0.37356 0.75794 0.493 0.6223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6125
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_16_count 0.613048075653218
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0335 -0.7935 0.0136 0.9437 4.0379
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00394 0.08812 113.525 <2e-16 ***
## category_code_LT01_4_count 1.03766 0.06944 14.944 <2e-16 ***
## category_code_LT01_5_count 0.93002 0.06356 14.632 <2e-16 ***
## category_code_LT01_8_count -0.17420 0.27794 -0.627 0.5311
## category_code_LT01_9_count 0.53260 0.22659 2.350 0.0191 *
## category_code_LT01_12_count 0.19884 0.20686 0.961 0.3369
## category_code_LT01_16_count 1.17320 1.17860 0.995 0.3200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6177, Adjusted R-squared: 0.613
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count 0.611972874390796
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0376 -0.7953 0.0186 0.9019 4.0361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00576 0.08832 113.289 <2e-16 ***
## category_code_LT01_4_count 1.04497 0.07023 14.878 <2e-16 ***
## category_code_LT01_5_count 0.93344 0.06370 14.653 <2e-16 ***
## category_code_LT01_8_count -0.14145 0.27827 -0.508 0.6115
## category_code_LT01_9_count 0.55205 0.22749 2.427 0.0156 *
## category_code_LT01_13_count 0.16889 0.24712 0.683 0.4947
## category_code_LT01_14_count 0.09293 0.33242 0.280 0.7799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.612
## F-statistic: 131.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count 0.612127970400281
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0378 -0.7941 0.0165 0.9237 4.0373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00454 0.08822 113.411 <2e-16 ***
## category_code_LT01_4_count 1.04207 0.06986 14.917 <2e-16 ***
## category_code_LT01_5_count 0.93548 0.06341 14.754 <2e-16 ***
## category_code_LT01_8_count -0.14171 0.27821 -0.509 0.6107
## category_code_LT01_9_count 0.55866 0.22695 2.462 0.0142 *
## category_code_LT01_13_count 0.17720 0.24755 0.716 0.4745
## category_code_LT01_15_count 0.39802 0.75967 0.524 0.6006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6168, Adjusted R-squared: 0.6121
## F-statistic: 131.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_16_count 0.612720946455445
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0370 -0.7944 0.0077 0.9337 4.0370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00486 0.08815 113.501 <2e-16 ***
## category_code_LT01_4_count 1.04491 0.06856 15.241 <2e-16 ***
## category_code_LT01_5_count 0.93382 0.06337 14.737 <2e-16 ***
## category_code_LT01_8_count -0.15373 0.27829 -0.552 0.5809
## category_code_LT01_9_count 0.54633 0.22696 2.407 0.0164 *
## category_code_LT01_13_count 0.17611 0.24698 0.713 0.4761
## category_code_LT01_16_count 1.19521 1.17955 1.013 0.3114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6127
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count 0.611783378674645
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0397 -0.7959 0.0145 0.9008 4.0356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00629 0.08834 113.274 <2e-16 ***
## category_code_LT01_4_count 1.04832 0.07006 14.964 <2e-16 ***
## category_code_LT01_5_count 0.93559 0.06367 14.694 <2e-16 ***
## category_code_LT01_8_count -0.15364 0.27793 -0.553 0.5807
## category_code_LT01_9_count 0.54524 0.22723 2.399 0.0168 *
## category_code_LT01_14_count 0.09173 0.33252 0.276 0.7828
## category_code_LT01_15_count 0.36156 0.75860 0.477 0.6339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6165, Adjusted R-squared: 0.6118
## F-statistic: 131.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_16_count 0.612408096035739
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0388 -0.7964 0.0060 0.9121 4.0350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00683 0.08827 113.370 <2e-16 ***
## category_code_LT01_4_count 1.04956 0.06899 15.213 <2e-16 ***
## category_code_LT01_5_count 0.93361 0.06363 14.672 <2e-16 ***
## category_code_LT01_8_count -0.16592 0.27802 -0.597 0.5509
## category_code_LT01_9_count 0.53225 0.22729 2.342 0.0196 *
## category_code_LT01_14_count 0.11120 0.33268 0.334 0.7383
## category_code_LT01_16_count 1.19229 1.18117 1.009 0.3133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6124
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_9_count+category_code_LT01_15_count+category_code_LT01_16_count 0.61252305714574
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0391 -0.7963 0.0040 0.9297 4.0364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00543 0.08817 113.485 <2e-16 ***
## category_code_LT01_4_count 1.04823 0.06840 15.326 <2e-16 ***
## category_code_LT01_5_count 0.93606 0.06334 14.779 <2e-16 ***
## category_code_LT01_8_count -0.16640 0.27797 -0.599 0.5497
## category_code_LT01_9_count 0.53927 0.22671 2.379 0.0178 *
## category_code_LT01_15_count 0.38464 0.75809 0.507 0.6121
## category_code_LT01_16_count 1.18778 1.17982 1.007 0.3146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6125
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.622280232314087
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0121 -0.7601 0.0333 0.9322 3.5788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98064 0.09045 110.345 < 2e-16 ***
## category_code_LT01_4_count 0.83702 0.08751 9.565 < 2e-16 ***
## category_code_LT01_5_count 0.93283 0.06265 14.890 < 2e-16 ***
## category_code_LT01_8_count -0.11173 0.27434 -0.407 0.684
## category_code_LT01_10_count 0.14659 0.11203 1.309 0.191
## category_code_LT01_11_count 0.46360 0.11414 4.062 5.67e-05 ***
## category_code_LT01_12_count -0.04067 0.21258 -0.191 0.848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.622308186914809
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0109 -0.7457 0.0356 0.9137 3.5860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98048 0.09044 110.353 < 2e-16 ***
## category_code_LT01_4_count 0.83428 0.08795 9.486 < 2e-16 ***
## category_code_LT01_5_count 0.93133 0.06247 14.908 < 2e-16 ***
## category_code_LT01_8_count -0.10975 0.27452 -0.400 0.689
## category_code_LT01_10_count 0.14501 0.11201 1.295 0.196
## category_code_LT01_11_count 0.45599 0.11002 4.145 4.01e-05 ***
## category_code_LT01_13_count 0.06589 0.24396 0.270 0.787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6269, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.622254778530659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0116 -0.7463 0.0339 0.9324 3.5850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98101 0.09091 109.792 < 2e-16 ***
## category_code_LT01_4_count 0.83592 0.08849 9.447 < 2e-16 ***
## category_code_LT01_5_count 0.93150 0.06275 14.845 < 2e-16 ***
## category_code_LT01_8_count -0.11417 0.27411 -0.417 0.677
## category_code_LT01_10_count 0.14444 0.11460 1.260 0.208
## category_code_LT01_11_count 0.45759 0.10986 4.165 3.67e-05 ***
## category_code_LT01_14_count 0.01987 0.33503 0.059 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.6222549795026
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0114 -0.7470 0.0346 0.9327 3.5847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98059 0.09047 110.322 < 2e-16 ***
## category_code_LT01_4_count 0.83622 0.08785 9.519 < 2e-16 ***
## category_code_LT01_5_count 0.93194 0.06246 14.921 < 2e-16 ***
## category_code_LT01_8_count -0.11418 0.27411 -0.417 0.677
## category_code_LT01_10_count 0.14534 0.11232 1.294 0.196
## category_code_LT01_11_count 0.45726 0.11006 4.155 3.84e-05 ***
## category_code_LT01_15_count 0.04624 0.75218 0.061 0.951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6223
## F-statistic: 137.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.622844059158684
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0116 -0.7413 0.0374 0.9112 3.5905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98156 0.09039 110.433 < 2e-16 ***
## category_code_LT01_4_count 0.83515 0.08745 9.550 < 2e-16 ***
## category_code_LT01_5_count 0.93079 0.06241 14.915 < 2e-16 ***
## category_code_LT01_8_count -0.12602 0.27422 -0.460 0.646
## category_code_LT01_10_count 0.14129 0.11201 1.261 0.208
## category_code_LT01_11_count 0.45393 0.10985 4.132 4.22e-05 ***
## category_code_LT01_16_count 1.02246 1.16469 0.878 0.380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6274, Adjusted R-squared: 0.6228
## F-statistic: 137.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.609762977376731
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0105 -0.7785 0.0273 0.9192 3.8794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97361 0.09192 108.505 <2e-16 ***
## category_code_LT01_4_count 1.05712 0.06975 15.156 <2e-16 ***
## category_code_LT01_5_count 0.94063 0.06367 14.773 <2e-16 ***
## category_code_LT01_8_count -0.14002 0.27920 -0.502 0.616
## category_code_LT01_10_count 0.17191 0.11372 1.512 0.131
## category_code_LT01_12_count 0.19091 0.20804 0.918 0.359
## category_code_LT01_13_count 0.11618 0.24770 0.469 0.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.609590001075104
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0114 -0.7796 0.0254 0.9267 3.8785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97401 0.09241 107.927 <2e-16 ***
## category_code_LT01_4_count 1.06207 0.06996 15.182 <2e-16 ***
## category_code_LT01_5_count 0.94131 0.06393 14.725 <2e-16 ***
## category_code_LT01_8_count -0.14791 0.27877 -0.531 0.596
## category_code_LT01_10_count 0.17239 0.11632 1.482 0.139
## category_code_LT01_12_count 0.19318 0.20845 0.927 0.355
## category_code_LT01_14_count 0.01658 0.34132 0.049 0.961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.609673221734455
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0120 -0.7797 0.0259 0.9312 3.8804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97421 0.09195 108.473 <2e-16 ***
## category_code_LT01_4_count 1.05881 0.06975 15.180 <2e-16 ***
## category_code_LT01_5_count 0.94189 0.06365 14.798 <2e-16 ***
## category_code_LT01_8_count -0.14874 0.27875 -0.534 0.594
## category_code_LT01_10_count 0.17043 0.11409 1.494 0.136
## category_code_LT01_12_count 0.19504 0.20801 0.938 0.349
## category_code_LT01_15_count 0.24973 0.76330 0.327 0.744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6097
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.610421809897726
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0115 -0.7816 0.0225 0.9310 3.8826
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97491 0.09185 108.599 <2e-16 ***
## category_code_LT01_4_count 1.05846 0.06879 15.387 <2e-16 ***
## category_code_LT01_5_count 0.94016 0.06360 14.782 <2e-16 ***
## category_code_LT01_8_count -0.16185 0.27880 -0.581 0.562
## category_code_LT01_10_count 0.16780 0.11371 1.476 0.141
## category_code_LT01_12_count 0.19444 0.20778 0.936 0.350
## category_code_LT01_16_count 1.21245 1.18282 1.025 0.306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.609104056596427
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0152 -0.7731 0.0247 0.9197 3.8753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97508 0.09247 107.879 <2e-16 ***
## category_code_LT01_4_count 1.07137 0.06911 15.502 <2e-16 ***
## category_code_LT01_5_count 0.94531 0.06377 14.825 <2e-16 ***
## category_code_LT01_8_count -0.13071 0.27925 -0.468 0.640
## category_code_LT01_10_count 0.17427 0.11639 1.497 0.135
## category_code_LT01_13_count 0.12325 0.24780 0.497 0.619
## category_code_LT01_14_count 0.03879 0.34079 0.114 0.909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6138, Adjusted R-squared: 0.6091
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.60918752303387
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0155 -0.7710 0.0237 0.9354 3.8763
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97471 0.09201 108.412 <2e-16 ***
## category_code_LT01_4_count 1.06873 0.06875 15.546 <2e-16 ***
## category_code_LT01_5_count 0.94633 0.06345 14.914 <2e-16 ***
## category_code_LT01_8_count -0.13096 0.27921 -0.469 0.639
## category_code_LT01_10_count 0.17375 0.11409 1.523 0.128
## category_code_LT01_13_count 0.12852 0.24828 0.518 0.605
## category_code_LT01_15_count 0.26267 0.76524 0.343 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6139, Adjusted R-squared: 0.6092
## F-statistic: 130.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.609950140274509
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0150 -0.7756 0.0270 0.9383 3.8785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97540 0.09191 108.540 <2e-16 ***
## category_code_LT01_4_count 1.06833 0.06770 15.780 <2e-16 ***
## category_code_LT01_5_count 0.94451 0.06340 14.898 <2e-16 ***
## category_code_LT01_8_count -0.14407 0.27924 -0.516 0.606
## category_code_LT01_10_count 0.17114 0.11370 1.505 0.133
## category_code_LT01_13_count 0.13128 0.24765 0.530 0.596
## category_code_LT01_16_count 1.22948 1.18414 1.038 0.300
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6147, Adjusted R-squared: 0.61
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.608984256262039
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0168 -0.7772 0.0223 0.9274 3.8759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97564 0.09250 107.841 <2e-16 ***
## category_code_LT01_4_count 1.07398 0.06897 15.572 <2e-16 ***
## category_code_LT01_5_count 0.94675 0.06374 14.853 <2e-16 ***
## category_code_LT01_8_count -0.13965 0.27883 -0.501 0.617
## category_code_LT01_10_count 0.17321 0.11674 1.484 0.139
## category_code_LT01_14_count 0.03821 0.34084 0.112 0.911
## category_code_LT01_15_count 0.23776 0.76387 0.311 0.756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.609
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609750624190189
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0166 -0.7804 0.0192 0.9315 3.8794
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97695 0.09241 107.966 <2e-16 ***
## category_code_LT01_4_count 1.07250 0.06808 15.753 <2e-16 ***
## category_code_LT01_5_count 0.94462 0.06370 14.830 <2e-16 ***
## category_code_LT01_8_count -0.15311 0.27889 -0.549 0.583
## category_code_LT01_10_count 0.16885 0.11645 1.450 0.148
## category_code_LT01_14_count 0.05891 0.34114 0.173 0.863
## category_code_LT01_16_count 1.22185 1.18604 1.030 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609820684640835
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0167 -0.7811 0.0243 0.9465 3.8792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97603 0.09194 108.502 <2e-16 ***
## category_code_LT01_4_count 1.07097 0.06756 15.853 <2e-16 ***
## category_code_LT01_5_count 0.94605 0.06338 14.927 <2e-16 ***
## category_code_LT01_8_count -0.15354 0.27885 -0.551 0.582
## category_code_LT01_10_count 0.16989 0.11407 1.489 0.137
## category_code_LT01_15_count 0.26223 0.76342 0.343 0.731
## category_code_LT01_16_count 1.22230 1.18433 1.032 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.621037019904149
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0444 -0.7353 0.0251 0.9002 3.7029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01281 0.08714 114.899 < 2e-16 ***
## category_code_LT01_4_count 0.84686 0.08760 9.667 < 2e-16 ***
## category_code_LT01_5_count 0.93305 0.06278 14.863 < 2e-16 ***
## category_code_LT01_8_count -0.09854 0.27517 -0.358 0.720
## category_code_LT01_11_count 0.47062 0.11428 4.118 4.48e-05 ***
## category_code_LT01_12_count -0.03262 0.21284 -0.153 0.878
## category_code_LT01_13_count 0.07565 0.24430 0.310 0.757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.621055579540123
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0450 -0.7352 0.0262 0.9115 3.7003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01433 0.08722 114.815 < 2e-16 ***
## category_code_LT01_4_count 0.84456 0.08838 9.556 < 2e-16 ***
## category_code_LT01_5_count 0.93163 0.06302 14.784 < 2e-16 ***
## category_code_LT01_8_count -0.10457 0.27473 -0.381 0.704
## category_code_LT01_11_count 0.47218 0.11412 4.137 4.13e-05 ***
## category_code_LT01_12_count -0.03676 0.21334 -0.172 0.863
## category_code_LT01_14_count 0.11383 0.32868 0.346 0.729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6211
## F-statistic: 136.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.620982650469813
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0451 -0.7385 0.0238 0.9124 3.7023
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01300 0.08715 114.897 < 2e-16 ***
## category_code_LT01_4_count 0.84833 0.08755 9.689 < 2e-16 ***
## category_code_LT01_5_count 0.93381 0.06276 14.879 < 2e-16 ***
## category_code_LT01_8_count -0.10404 0.27476 -0.379 0.705
## category_code_LT01_11_count 0.47113 0.11444 4.117 4.5e-05 ***
## category_code_LT01_12_count -0.03043 0.21294 -0.143 0.886
## category_code_LT01_15_count 0.11986 0.75148 0.159 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.62163611054989
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0443 -0.7347 0.0253 0.9068 3.7046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01308 0.08707 114.997 < 2e-16 ***
## category_code_LT01_4_count 0.84761 0.08708 9.734 < 2e-16 ***
## category_code_LT01_5_count 0.93241 0.06271 14.869 < 2e-16 ***
## category_code_LT01_8_count -0.11664 0.27486 -0.424 0.671
## category_code_LT01_11_count 0.46777 0.11414 4.098 4.87e-05 ***
## category_code_LT01_12_count -0.02910 0.21266 -0.137 0.891
## category_code_LT01_16_count 1.08915 1.16537 0.935 0.350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6216
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.621105808173853
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0434 -0.7346 0.0297 0.9053 3.7059
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01378 0.08720 114.842 < 2e-16 ***
## category_code_LT01_4_count 0.84155 0.08885 9.471 < 2e-16 ***
## category_code_LT01_5_count 0.93020 0.06287 14.795 < 2e-16 ***
## category_code_LT01_8_count -0.10182 0.27490 -0.370 0.711
## category_code_LT01_11_count 0.46483 0.10997 4.227 2.83e-05 ***
## category_code_LT01_13_count 0.07520 0.24425 0.308 0.758
## category_code_LT01_14_count 0.11003 0.32784 0.336 0.737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6257, Adjusted R-squared: 0.6211
## F-statistic: 136.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.621045244139909
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0437 -0.7339 0.0267 0.9093 3.7075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01253 0.08713 114.919 < 2e-16 ***
## category_code_LT01_4_count 0.84487 0.08807 9.593 < 2e-16 ***
## category_code_LT01_5_count 0.93247 0.06258 14.900 < 2e-16 ***
## category_code_LT01_8_count -0.10089 0.27490 -0.367 0.714
## category_code_LT01_11_count 0.46441 0.11021 4.214 2.99e-05 ***
## category_code_LT01_13_count 0.07800 0.24479 0.319 0.750
## category_code_LT01_15_count 0.13908 0.75263 0.185 0.853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.621
## F-statistic: 136.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.62170994213125
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0428 -0.7332 0.0290 0.9019 3.7096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01261 0.08705 115.021 < 2e-16 ***
## category_code_LT01_4_count 0.84418 0.08754 9.643 < 2e-16 ***
## category_code_LT01_5_count 0.93102 0.06253 14.889 < 2e-16 ***
## category_code_LT01_8_count -0.11322 0.27498 -0.412 0.681
## category_code_LT01_11_count 0.46125 0.10995 4.195 3.24e-05 ***
## category_code_LT01_13_count 0.08265 0.24418 0.338 0.735
## category_code_LT01_16_count 1.10410 1.16580 0.947 0.344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.621052688668928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0442 -0.7346 0.0283 0.9169 3.7052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01397 0.08720 114.840 < 2e-16 ***
## category_code_LT01_4_count 0.84304 0.08878 9.496 < 2e-16 ***
## category_code_LT01_5_count 0.93102 0.06286 14.812 < 2e-16 ***
## category_code_LT01_8_count -0.10716 0.27450 -0.390 0.696
## category_code_LT01_11_count 0.46564 0.11003 4.232 2.77e-05 ***
## category_code_LT01_14_count 0.10932 0.32787 0.333 0.739
## category_code_LT01_15_count 0.12098 0.75104 0.161 0.872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.6211
## F-statistic: 136.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.62173446430141
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0433 -0.7339 0.0308 0.9160 3.7072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01425 0.08712 114.946 < 2e-16 ***
## category_code_LT01_4_count 0.84156 0.08834 9.526 < 2e-16 ***
## category_code_LT01_5_count 0.92933 0.06281 14.797 < 2e-16 ***
## category_code_LT01_8_count -0.12019 0.27460 -0.438 0.662
## category_code_LT01_11_count 0.46224 0.10979 4.210 3.04e-05 ***
## category_code_LT01_14_count 0.12549 0.32797 0.383 0.702
## category_code_LT01_16_count 1.11344 1.16659 0.954 0.340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.621650375986363
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0437 -0.7339 0.0267 0.9122 3.7088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01283 0.08705 115.018 < 2e-16 ***
## category_code_LT01_4_count 0.84566 0.08748 9.667 < 2e-16 ***
## category_code_LT01_5_count 0.93193 0.06251 14.908 < 2e-16 ***
## category_code_LT01_8_count -0.11908 0.27461 -0.434 0.665
## category_code_LT01_11_count 0.46205 0.11001 4.200 3.17e-05 ***
## category_code_LT01_15_count 0.14488 0.75076 0.193 0.847
## category_code_LT01_16_count 1.09795 1.16579 0.942 0.347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.608056793756938
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0490 -0.7944 0.0069 0.9203 4.0289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01295 0.08871 112.877 <2e-16 ***
## category_code_LT01_4_count 1.07077 0.07045 15.200 <2e-16 ***
## category_code_LT01_5_count 0.93949 0.06408 14.660 <2e-16 ***
## category_code_LT01_8_count -0.13148 0.27975 -0.470 0.639
## category_code_LT01_12_count 0.20076 0.20884 0.961 0.337
## category_code_LT01_13_count 0.12820 0.24812 0.517 0.606
## category_code_LT01_14_count 0.12399 0.33426 0.371 0.711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 491 degrees of freedom
## Multiple R-squared: 0.6128, Adjusted R-squared: 0.6081
## F-statistic: 129.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.608135702087958
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0493 -0.7948 0.0217 0.9395 4.0304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01144 0.08861 112.977 <2e-16 ***
## category_code_LT01_4_count 1.06985 0.07006 15.270 <2e-16 ***
## category_code_LT01_5_count 0.94204 0.06380 14.765 <2e-16 ***
## category_code_LT01_8_count -0.13139 0.27971 -0.470 0.639
## category_code_LT01_12_count 0.20775 0.20823 0.998 0.319
## category_code_LT01_13_count 0.13531 0.24855 0.544 0.586
## category_code_LT01_15_count 0.37130 0.76349 0.486 0.627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 491 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.6081
## F-statistic: 129.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.608934903762623
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0480 -0.8011 0.0152 0.9397 4.0303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01159 0.08852 113.094 <2e-16 ***
## category_code_LT01_4_count 1.07078 0.06882 15.559 <2e-16 ***
## category_code_LT01_5_count 0.94002 0.06375 14.745 <2e-16 ***
## category_code_LT01_8_count -0.14530 0.27974 -0.519 0.604
## category_code_LT01_12_count 0.20644 0.20801 0.992 0.321
## category_code_LT01_13_count 0.13633 0.24795 0.550 0.583
## category_code_LT01_16_count 1.31885 1.18416 1.114 0.266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6089
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.608004880992926
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0503 -0.7971 0.0028 0.9259 4.0287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01322 0.08871 112.875 <2e-16 ***
## category_code_LT01_4_count 1.07142 0.07053 15.192 <2e-16 ***
## category_code_LT01_5_count 0.94098 0.06406 14.689 <2e-16 ***
## category_code_LT01_8_count -0.14145 0.27930 -0.506 0.613
## category_code_LT01_12_count 0.20552 0.20876 0.984 0.325
## category_code_LT01_14_count 0.12165 0.33431 0.364 0.716
## category_code_LT01_15_count 0.34253 0.76225 0.449 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 491 degrees of freedom
## Multiple R-squared: 0.6127, Adjusted R-squared: 0.608
## F-statistic: 129.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608837402819971
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0491 -0.8032 0.0018 0.9274 4.0283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01359 0.08862 112.999 <2e-16 ***
## category_code_LT01_4_count 1.07095 0.06950 15.409 <2e-16 ***
## category_code_LT01_5_count 0.93863 0.06401 14.664 <2e-16 ***
## category_code_LT01_8_count -0.15581 0.27935 -0.558 0.577
## category_code_LT01_12_count 0.20340 0.20852 0.975 0.330
## category_code_LT01_14_count 0.14178 0.33433 0.424 0.672
## category_code_LT01_16_count 1.32368 1.18519 1.117 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608881327579207
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0494 -0.8015 0.0109 0.9453 4.0300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01189 0.08853 113.093 <2e-16 ***
## category_code_LT01_4_count 1.07128 0.06894 15.539 <2e-16 ***
## category_code_LT01_5_count 0.94157 0.06373 14.775 <2e-16 ***
## category_code_LT01_8_count -0.15590 0.27933 -0.558 0.577
## category_code_LT01_12_count 0.21141 0.20793 1.017 0.310
## category_code_LT01_15_count 0.36923 0.76163 0.485 0.628
## category_code_LT01_16_count 1.31488 1.18415 1.110 0.267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6089
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.6074954342564
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0548 -0.7984 -0.0073 0.9233 4.0271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01477 0.08875 112.845 <2e-16 ***
## category_code_LT01_4_count 1.08061 0.06977 15.489 <2e-16 ***
## category_code_LT01_5_count 0.94514 0.06390 14.791 <2e-16 ***
## category_code_LT01_8_count -0.12235 0.27976 -0.437 0.662
## category_code_LT01_13_count 0.14297 0.24863 0.575 0.566
## category_code_LT01_14_count 0.14647 0.33357 0.439 0.661
## category_code_LT01_15_count 0.35890 0.76411 0.470 0.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.411 on 491 degrees of freedom
## Multiple R-squared: 0.6122, Adjusted R-squared: 0.6075
## F-statistic: 129.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608349729806784
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0534 -0.7973 -0.0048 0.9249 4.0267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01513 0.08865 112.972 <2e-16 ***
## category_code_LT01_4_count 1.08005 0.06866 15.731 <2e-16 ***
## category_code_LT01_5_count 0.94267 0.06385 14.763 <2e-16 ***
## category_code_LT01_8_count -0.13695 0.27978 -0.489 0.625
## category_code_LT01_13_count 0.14438 0.24801 0.582 0.561
## category_code_LT01_14_count 0.16677 0.33358 0.500 0.617
## category_code_LT01_16_count 1.34866 1.18647 1.137 0.256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6083
## F-statistic: 129.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608356399116035
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0540 -0.7979 0.0012 0.9370 4.0287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01320 0.08857 113.050 <2e-16 ***
## category_code_LT01_4_count 1.08165 0.06788 15.934 <2e-16 ***
## category_code_LT01_5_count 0.94627 0.06353 14.896 <2e-16 ***
## category_code_LT01_8_count -0.13590 0.27975 -0.486 0.627
## category_code_LT01_13_count 0.15201 0.24849 0.612 0.541
## category_code_LT01_15_count 0.38806 0.76355 0.508 0.612
## category_code_LT01_16_count 1.33742 1.18555 1.128 0.260
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6084
## F-statistic: 129.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_8_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608252668444164
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0550 -0.8050 -0.0071 0.9350 4.0264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01549 0.08866 112.965 <2e-16 ***
## category_code_LT01_4_count 1.08175 0.06859 15.771 <2e-16 ***
## category_code_LT01_5_count 0.94446 0.06382 14.798 <2e-16 ***
## category_code_LT01_8_count -0.14772 0.27940 -0.529 0.597
## category_code_LT01_14_count 0.16485 0.33364 0.494 0.621
## category_code_LT01_15_count 0.35522 0.76221 0.466 0.641
## category_code_LT01_16_count 1.34257 1.18650 1.132 0.258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 491 degrees of freedom
## Multiple R-squared: 0.613, Adjusted R-squared: 0.6083
## F-statistic: 129.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count 0.624803708348644
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0017 -0.7613 0.0290 0.9176 3.6230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97900 0.09012 110.726 < 2e-16 ***
## category_code_LT01_4_count 0.81666 0.08778 9.303 < 2e-16 ***
## category_code_LT01_5_count 0.92011 0.06194 14.855 < 2e-16 ***
## category_code_LT01_9_count 0.42029 0.22564 1.863 0.06311 .
## category_code_LT01_10_count 0.11949 0.11250 1.062 0.28869
## category_code_LT01_11_count 0.44527 0.11419 3.899 0.00011 ***
## category_code_LT01_12_count -0.04118 0.21168 -0.195 0.84584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count 0.624910289440057
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0003 -0.7586 0.0377 0.9184 3.6320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97886 0.09011 110.747 < 2e-16 ***
## category_code_LT01_4_count 0.81231 0.08826 9.204 < 2e-16 ***
## category_code_LT01_5_count 0.91831 0.06171 14.880 < 2e-16 ***
## category_code_LT01_9_count 0.42707 0.22612 1.889 0.0595 .
## category_code_LT01_10_count 0.11704 0.11248 1.040 0.2986
## category_code_LT01_11_count 0.43630 0.11016 3.961 8.58e-05 ***
## category_code_LT01_13_count 0.10246 0.24327 0.421 0.6738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6294, Adjusted R-squared: 0.6249
## F-statistic: 139 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count 0.62477515388465
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0006 -0.7622 0.0380 0.9208 3.6278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.978613 0.090581 110.163 < 2e-16 ***
## category_code_LT01_4_count 0.816582 0.088688 9.207 < 2e-16 ***
## category_code_LT01_5_count 0.919182 0.061992 14.827 < 2e-16 ***
## category_code_LT01_9_count 0.420850 0.225837 1.864 0.063 .
## category_code_LT01_10_count 0.119248 0.114925 1.038 0.300
## category_code_LT01_11_count 0.439284 0.109954 3.995 7.46e-05 ***
## category_code_LT01_14_count -0.007284 0.334177 -0.022 0.983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count 0.62478531321211
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0010 -0.7613 0.0387 0.9226 3.6297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97903 0.09014 110.707 < 2e-16 ***
## category_code_LT01_4_count 0.81534 0.08815 9.250 < 2e-16 ***
## category_code_LT01_5_count 0.91916 0.06170 14.896 < 2e-16 ***
## category_code_LT01_9_count 0.42150 0.22576 1.867 0.0625 .
## category_code_LT01_10_count 0.11763 0.11282 1.043 0.2976
## category_code_LT01_11_count 0.43844 0.11017 3.980 7.95e-05 ***
## category_code_LT01_15_count 0.08801 0.75001 0.117 0.9066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.6248
## F-statistic: 138.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_16_count 0.625250436629683
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0008 -0.7587 0.0433 0.9172 3.6335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97972 0.09007 110.797 < 2e-16 ***
## category_code_LT01_4_count 0.81510 0.08772 9.292 < 2e-16 ***
## category_code_LT01_5_count 0.91785 0.06168 14.881 < 2e-16 ***
## category_code_LT01_9_count 0.41397 0.22566 1.834 0.0672 .
## category_code_LT01_10_count 0.11492 0.11246 1.022 0.3074
## category_code_LT01_11_count 0.43631 0.10994 3.968 8.31e-05 ***
## category_code_LT01_16_count 0.91600 1.16034 0.789 0.4302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.379 on 491 degrees of freedom
## Multiple R-squared: 0.6298, Adjusted R-squared: 0.6253
## F-statistic: 139.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count 0.613508198500968
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9976 -0.7741 0.0032 0.9356 3.9194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97198 0.09145 109.041 <2e-16 ***
## category_code_LT01_4_count 1.01948 0.07131 14.297 <2e-16 ***
## category_code_LT01_5_count 0.92441 0.06287 14.703 <2e-16 ***
## category_code_LT01_9_count 0.51145 0.22846 2.239 0.0256 *
## category_code_LT01_10_count 0.13702 0.11414 1.201 0.2305
## category_code_LT01_12_count 0.17796 0.20694 0.860 0.3902
## category_code_LT01_13_count 0.15783 0.24654 0.640 0.5224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 491 degrees of freedom
## Multiple R-squared: 0.6182, Adjusted R-squared: 0.6135
## F-statistic: 132.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count 0.613187265992186
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9980 -0.7901 0.0018 0.9312 3.9157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97140 0.09196 108.429 <2e-16 ***
## category_code_LT01_4_count 1.02822 0.07126 14.430 <2e-16 ***
## category_code_LT01_5_count 0.92583 0.06314 14.664 <2e-16 ***
## category_code_LT01_9_count 0.50281 0.22830 2.202 0.0281 *
## category_code_LT01_10_count 0.14090 0.11658 1.209 0.2274
## category_code_LT01_12_count 0.18245 0.20738 0.880 0.3794
## category_code_LT01_14_count -0.01566 0.34002 -0.046 0.9633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6179, Adjusted R-squared: 0.6132
## F-statistic: 132.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count 0.613299726228343
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9992 -0.7835 0.0116 0.9216 3.9199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97258 0.09150 108.994 <2e-16 ***
## category_code_LT01_4_count 1.02298 0.07124 14.360 <2e-16 ***
## category_code_LT01_5_count 0.92582 0.06287 14.726 <2e-16 ***
## category_code_LT01_9_count 0.50463 0.22815 2.212 0.0274 *
## category_code_LT01_10_count 0.13599 0.11452 1.187 0.2356
## category_code_LT01_12_count 0.18314 0.20693 0.885 0.3766
## category_code_LT01_15_count 0.28930 0.75996 0.381 0.7036
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.618, Adjusted R-squared: 0.6133
## F-statistic: 132.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_16_count 0.613840943921752
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9983 -0.7822 0.0077 0.9384 3.9205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97294 0.09142 109.090 <2e-16 ***
## category_code_LT01_4_count 1.02443 0.07021 14.590 <2e-16 ***
## category_code_LT01_5_count 0.92405 0.06284 14.704 <2e-16 ***
## category_code_LT01_9_count 0.49386 0.22811 2.165 0.0309 *
## category_code_LT01_10_count 0.13511 0.11412 1.184 0.2370
## category_code_LT01_12_count 0.18215 0.20676 0.881 0.3788
## category_code_LT01_16_count 1.07458 1.17719 0.913 0.3618
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 491 degrees of freedom
## Multiple R-squared: 0.6185, Adjusted R-squared: 0.6138
## F-statistic: 132.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count 0.612926257532182
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0018 -0.7692 -0.0078 0.9197 3.9140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.972571 0.091987 108.413 <2e-16 ***
## category_code_LT01_4_count 1.033864 0.070615 14.641 <2e-16 ***
## category_code_LT01_5_count 0.929564 0.062925 14.773 <2e-16 ***
## category_code_LT01_9_count 0.515134 0.228786 2.252 0.0248 *
## category_code_LT01_10_count 0.141342 0.116641 1.212 0.2262
## category_code_LT01_13_count 0.164038 0.246628 0.665 0.5063
## category_code_LT01_14_count 0.005273 0.339406 0.016 0.9876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6176, Adjusted R-squared: 0.6129
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count 0.613059181254973
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0027 -0.7814 -0.0007 0.8992 3.9176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97324 0.09152 108.968 <2e-16 ***
## category_code_LT01_4_count 1.02878 0.07049 14.595 <2e-16 ***
## category_code_LT01_5_count 0.92993 0.06262 14.851 <2e-16 ***
## category_code_LT01_9_count 0.51814 0.22865 2.266 0.0239 *
## category_code_LT01_10_count 0.13752 0.11453 1.201 0.2304
## category_code_LT01_13_count 0.17077 0.24713 0.691 0.4899
## category_code_LT01_15_count 0.31308 0.76178 0.411 0.6813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6177, Adjusted R-squared: 0.6131
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_16_count 0.613611204108539
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0017 -0.7674 -0.0103 0.9253 3.9180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97357 0.09145 109.065 <2e-16 ***
## category_code_LT01_4_count 1.03040 0.06936 14.856 <2e-16 ***
## category_code_LT01_5_count 0.92807 0.06259 14.828 <2e-16 ***
## category_code_LT01_9_count 0.50701 0.22855 2.218 0.027 *
## category_code_LT01_10_count 0.13680 0.11411 1.199 0.231
## category_code_LT01_13_count 0.17147 0.24654 0.696 0.487
## category_code_LT01_16_count 1.09931 1.17815 0.933 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 491 degrees of freedom
## Multiple R-squared: 0.6183, Adjusted R-squared: 0.6136
## F-statistic: 132.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count 0.612683011812017
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0035 -0.7846 0.0027 0.9137 3.9141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.973134 0.092037 108.360 <2e-16 ***
## category_code_LT01_4_count 1.038339 0.070402 14.749 <2e-16 ***
## category_code_LT01_5_count 0.931179 0.062917 14.800 <2e-16 ***
## category_code_LT01_9_count 0.507988 0.228505 2.223 0.0267 *
## category_code_LT01_10_count 0.140751 0.117018 1.203 0.2296
## category_code_LT01_14_count 0.004697 0.339512 0.014 0.9890
## category_code_LT01_15_count 0.278114 0.760470 0.366 0.7147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6127
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_16_count 0.61323424751969
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0029 -0.7736 -0.0051 0.9229 3.9158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97404 0.09197 108.452 <2e-16 ***
## category_code_LT01_4_count 1.03878 0.06945 14.957 <2e-16 ***
## category_code_LT01_5_count 0.92906 0.06290 14.770 <2e-16 ***
## category_code_LT01_9_count 0.49673 0.22848 2.174 0.0302 *
## category_code_LT01_10_count 0.13835 0.11669 1.186 0.2363
## category_code_LT01_14_count 0.02331 0.33992 0.069 0.9453
## category_code_LT01_16_count 1.07780 1.18039 0.913 0.3616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6179, Adjusted R-squared: 0.6132
## F-statistic: 132.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_10_count+category_code_LT01_15_count+category_code_LT01_16_count 0.613352586662806
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0035 -0.7815 -0.0024 0.9218 3.9182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97418 0.09150 109.010 <2e-16 ***
## category_code_LT01_4_count 1.03496 0.06914 14.969 <2e-16 ***
## category_code_LT01_5_count 0.92977 0.06258 14.857 <2e-16 ***
## category_code_LT01_9_count 0.49969 0.22829 2.189 0.0291 *
## category_code_LT01_10_count 0.13608 0.11450 1.188 0.2352
## category_code_LT01_15_count 0.29925 0.76015 0.394 0.6940
## category_code_LT01_16_count 1.08680 1.17847 0.922 0.3569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 491 degrees of freedom
## Multiple R-squared: 0.618, Adjusted R-squared: 0.6134
## F-statistic: 132.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.624103856088928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0269 -0.7532 0.0378 0.9165 3.7269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00465 0.08682 115.233 < 2e-16 ***
## category_code_LT01_4_count 0.82108 0.08800 9.330 < 2e-16 ***
## category_code_LT01_5_count 0.91958 0.06202 14.827 < 2e-16 ***
## category_code_LT01_9_count 0.45663 0.22455 2.034 0.042537 *
## category_code_LT01_11_count 0.44773 0.11438 3.914 0.000103 ***
## category_code_LT01_12_count -0.03472 0.21177 -0.164 0.869822
## category_code_LT01_13_count 0.11204 0.24340 0.460 0.645491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6286, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.623975096838337
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0278 -0.7556 0.0346 0.9437 3.7235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00583 0.08692 115.111 < 2e-16 ***
## category_code_LT01_4_count 0.82261 0.08861 9.283 < 2e-16 ***
## category_code_LT01_5_count 0.91919 0.06226 14.764 < 2e-16 ***
## category_code_LT01_9_count 0.44694 0.22464 1.990 0.0472 *
## category_code_LT01_11_count 0.45088 0.11420 3.948 9.02e-05 ***
## category_code_LT01_12_count -0.03658 0.21233 -0.172 0.8633
## category_code_LT01_14_count 0.06859 0.32812 0.209 0.8345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.623972061217594
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0279 -0.7618 0.0364 0.9355 3.7256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00494 0.08683 115.220 <2e-16 ***
## category_code_LT01_4_count 0.82378 0.08794 9.368 <2e-16 ***
## category_code_LT01_5_count 0.92051 0.06201 14.844 <2e-16 ***
## category_code_LT01_9_count 0.45107 0.22420 2.012 0.0448 *
## category_code_LT01_11_count 0.44918 0.11452 3.922 0.0001 ***
## category_code_LT01_12_count -0.03203 0.21190 -0.151 0.8799
## category_code_LT01_15_count 0.14921 0.74862 0.199 0.8421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.624470662026761
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7558 0.0395 0.9353 3.7268
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00506 0.08678 115.298 < 2e-16 ***
## category_code_LT01_4_count 0.82398 0.08744 9.423 < 2e-16 ***
## category_code_LT01_5_count 0.91904 0.06199 14.827 < 2e-16 ***
## category_code_LT01_9_count 0.44205 0.22420 1.972 0.049205 *
## category_code_LT01_11_count 0.44729 0.11420 3.917 0.000103 ***
## category_code_LT01_12_count -0.03166 0.21165 -0.150 0.881156
## category_code_LT01_16_count 0.96519 1.16053 0.832 0.405993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.624112858549608
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0260 -0.7497 0.0420 0.9177 3.7303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00511 0.08689 115.153 < 2e-16 ***
## category_code_LT01_4_count 0.81795 0.08912 9.178 < 2e-16 ***
## category_code_LT01_5_count 0.91750 0.06207 14.783 < 2e-16 ***
## category_code_LT01_9_count 0.45381 0.22505 2.016 0.0443 *
## category_code_LT01_11_count 0.44221 0.11013 4.016 6.86e-05 ***
## category_code_LT01_13_count 0.11143 0.24338 0.458 0.6473
## category_code_LT01_14_count 0.06433 0.32722 0.197 0.8442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.62412586473013
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0261 -0.7588 0.0415 0.8971 3.7321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00429 0.08680 115.257 < 2e-16 ***
## category_code_LT01_4_count 0.81849 0.08850 9.249 < 2e-16 ***
## category_code_LT01_5_count 0.91887 0.06178 14.873 < 2e-16 ***
## category_code_LT01_9_count 0.45814 0.22462 2.040 0.0419 *
## category_code_LT01_11_count 0.44075 0.11039 3.993 7.54e-05 ***
## category_code_LT01_13_count 0.11539 0.24393 0.473 0.6364
## category_code_LT01_15_count 0.17686 0.74979 0.236 0.8136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6287, Adjusted R-squared: 0.6241
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.624634322183792
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0251 -0.7459 0.0479 0.8956 3.7332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00441 0.08674 115.337 < 2e-16 ***
## category_code_LT01_4_count 0.81887 0.08795 9.311 < 2e-16 ***
## category_code_LT01_5_count 0.91733 0.06175 14.854 < 2e-16 ***
## category_code_LT01_9_count 0.44895 0.22458 1.999 0.0462 *
## category_code_LT01_11_count 0.43903 0.11011 3.987 7.7e-05 ***
## category_code_LT01_13_count 0.11833 0.24334 0.486 0.6270
## category_code_LT01_16_count 0.98557 1.16085 0.849 0.3963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.6246
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.623983649874717
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0270 -0.7582 0.0403 0.9372 3.7287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00541 0.08690 115.141 < 2e-16 ***
## category_code_LT01_4_count 0.82065 0.08903 9.218 < 2e-16 ***
## category_code_LT01_5_count 0.91852 0.06206 14.801 < 2e-16 ***
## category_code_LT01_9_count 0.44831 0.22469 1.995 0.0466 *
## category_code_LT01_11_count 0.44402 0.11016 4.031 6.44e-05 ***
## category_code_LT01_14_count 0.06379 0.32730 0.195 0.8455
## category_code_LT01_15_count 0.15122 0.74827 0.202 0.8399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.381 on 491 degrees of freedom
## Multiple R-squared: 0.6285, Adjusted R-squared: 0.624
## F-statistic: 138.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.624498025816454
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0260 -0.7474 0.0467 0.9372 3.7298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00571 0.08684 115.223 < 2e-16 ***
## category_code_LT01_4_count 0.82020 0.08858 9.259 < 2e-16 ***
## category_code_LT01_5_count 0.91676 0.06204 14.778 < 2e-16 ***
## category_code_LT01_9_count 0.43844 0.22472 1.951 0.0516 .
## category_code_LT01_11_count 0.44204 0.10991 4.022 6.68e-05 ***
## category_code_LT01_14_count 0.07898 0.32750 0.241 0.8095
## category_code_LT01_16_count 0.98153 1.16201 0.845 0.3987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.624493490050205
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0262 -0.7527 0.0470 0.9199 3.7315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00474 0.08675 115.324 < 2e-16 ***
## category_code_LT01_4_count 0.82161 0.08787 9.351 < 2e-16 ***
## category_code_LT01_5_count 0.91842 0.06175 14.874 < 2e-16 ***
## category_code_LT01_9_count 0.44324 0.22424 1.977 0.0486 *
## category_code_LT01_11_count 0.44089 0.11015 4.003 7.23e-05 ***
## category_code_LT01_15_count 0.17094 0.74802 0.229 0.8193
## category_code_LT01_16_count 0.97454 1.16092 0.839 0.4016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 491 degrees of freedom
## Multiple R-squared: 0.629, Adjusted R-squared: 0.6245
## F-statistic: 138.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.61240707514397
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0276 -0.7920 0.0325 0.9449 4.0393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00255 0.08825 113.343 <2e-16 ***
## category_code_LT01_4_count 1.02921 0.07206 14.283 <2e-16 ***
## category_code_LT01_5_count 0.92352 0.06322 14.608 <2e-16 ***
## category_code_LT01_9_count 0.54433 0.22731 2.395 0.017 *
## category_code_LT01_12_count 0.18667 0.20757 0.899 0.369
## category_code_LT01_13_count 0.16905 0.24672 0.685 0.494
## category_code_LT01_14_count 0.06852 0.33313 0.206 0.837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6124
## F-statistic: 131.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.612594970622625
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0276 -0.7911 0.0354 0.9328 4.0403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00158 0.08814 113.478 <2e-16 ***
## category_code_LT01_4_count 1.02472 0.07199 14.235 <2e-16 ***
## category_code_LT01_5_count 0.92497 0.06295 14.694 <2e-16 ***
## category_code_LT01_9_count 0.54968 0.22678 2.424 0.0157 *
## category_code_LT01_12_count 0.19099 0.20696 0.923 0.3565
## category_code_LT01_13_count 0.17724 0.24714 0.717 0.4736
## category_code_LT01_15_count 0.40208 0.75923 0.530 0.5966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6173, Adjusted R-squared: 0.6126
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.613142214349799
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0264 -0.7911 0.0283 0.9481 4.0401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00180 0.08807 113.561 <2e-16 ***
## category_code_LT01_4_count 1.02778 0.07069 14.539 <2e-16 ***
## category_code_LT01_5_count 0.92299 0.06293 14.668 <2e-16 ***
## category_code_LT01_9_count 0.53735 0.22682 2.369 0.0182 *
## category_code_LT01_12_count 0.18955 0.20680 0.917 0.3598
## category_code_LT01_13_count 0.17652 0.24660 0.716 0.4745
## category_code_LT01_16_count 1.16305 1.17761 0.988 0.3238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6178, Adjusted R-squared: 0.6131
## F-statistic: 132.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.61222052695852
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0290 -0.7925 0.0277 0.9428 4.0390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00291 0.08827 113.323 <2e-16 ***
## category_code_LT01_4_count 1.03198 0.07204 14.324 <2e-16 ***
## category_code_LT01_5_count 0.92511 0.06321 14.635 <2e-16 ***
## category_code_LT01_9_count 0.53708 0.22702 2.366 0.0184 *
## category_code_LT01_12_count 0.19240 0.20754 0.927 0.3543
## category_code_LT01_14_count 0.06643 0.33324 0.199 0.8421
## category_code_LT01_15_count 0.36601 0.75818 0.483 0.6295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6169, Adjusted R-squared: 0.6122
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.612790296574112
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0279 -0.7926 0.0224 0.9464 4.0385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00334 0.08820 113.411 <2e-16 ***
## category_code_LT01_4_count 1.03358 0.07097 14.564 <2e-16 ***
## category_code_LT01_5_count 0.92284 0.06319 14.604 <2e-16 ***
## category_code_LT01_9_count 0.52417 0.22711 2.308 0.0214 *
## category_code_LT01_12_count 0.19019 0.20737 0.917 0.3595
## category_code_LT01_14_count 0.08544 0.33341 0.256 0.7979
## category_code_LT01_16_count 1.15286 1.17916 0.978 0.3287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.6128
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.612944707656222
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0280 -0.7918 0.0231 0.9460 4.0397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00220 0.08809 113.540 <2e-16 ***
## category_code_LT01_4_count 1.03051 0.07071 14.575 <2e-16 ***
## category_code_LT01_5_count 0.92463 0.06292 14.696 <2e-16 ***
## category_code_LT01_9_count 0.52980 0.22654 2.339 0.0198 *
## category_code_LT01_12_count 0.19546 0.20677 0.945 0.3450
## category_code_LT01_15_count 0.38750 0.75767 0.511 0.6093
## category_code_LT01_16_count 1.15305 1.17776 0.979 0.3281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 491 degrees of freedom
## Multiple R-squared: 0.6176, Adjusted R-squared: 0.6129
## F-statistic: 132.2 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.611979158187983
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0330 -0.7938 0.0270 0.9044 4.0377
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00420 0.08828 113.328 <2e-16 ***
## category_code_LT01_4_count 1.03691 0.07153 14.497 <2e-16 ***
## category_code_LT01_5_count 0.92899 0.06300 14.746 <2e-16 ***
## category_code_LT01_9_count 0.55089 0.22743 2.422 0.0158 *
## category_code_LT01_13_count 0.18393 0.24722 0.744 0.4573
## category_code_LT01_14_count 0.08861 0.33242 0.267 0.7899
## category_code_LT01_15_count 0.39215 0.75986 0.516 0.6060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.403 on 491 degrees of freedom
## Multiple R-squared: 0.6167, Adjusted R-squared: 0.612
## F-statistic: 131.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.612563373237062
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0318 -0.7942 0.0185 0.9105 4.0372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00463 0.08821 113.418 <2e-16 ***
## category_code_LT01_4_count 1.03868 0.07035 14.764 <2e-16 ***
## category_code_LT01_5_count 0.92658 0.06298 14.712 <2e-16 ***
## category_code_LT01_9_count 0.53748 0.22749 2.363 0.0185 *
## category_code_LT01_13_count 0.18344 0.24666 0.744 0.4574
## category_code_LT01_14_count 0.10795 0.33258 0.325 0.7456
## category_code_LT01_16_count 1.18424 1.18005 1.004 0.3161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6172, Adjusted R-squared: 0.6126
## F-statistic: 132 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.612717220471157
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0320 -0.7928 0.0187 0.9351 4.0387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00323 0.08811 113.536 <2e-16 ***
## category_code_LT01_4_count 1.03616 0.06994 14.814 <2e-16 ***
## category_code_LT01_5_count 0.92891 0.06266 14.824 <2e-16 ***
## category_code_LT01_9_count 0.54490 0.22690 2.402 0.0167 *
## category_code_LT01_13_count 0.19216 0.24712 0.778 0.4372
## category_code_LT01_15_count 0.41623 0.75937 0.548 0.5839
## category_code_LT01_16_count 1.18278 1.17872 1.003 0.3161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.6174, Adjusted R-squared: 0.6127
## F-statistic: 132.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_9_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.6123209716505
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0335 -0.7946 0.0149 0.9073 4.0368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.00509 0.08823 113.391 <2e-16 ***
## category_code_LT01_4_count 1.04259 0.07019 14.854 <2e-16 ***
## category_code_LT01_5_count 0.92848 0.06297 14.744 <2e-16 ***
## category_code_LT01_9_count 0.52976 0.22724 2.331 0.0201 *
## category_code_LT01_14_count 0.10637 0.33270 0.320 0.7493
## category_code_LT01_15_count 0.37589 0.75827 0.496 0.6203
## category_code_LT01_16_count 1.17249 1.18025 0.993 0.3210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 491 degrees of freedom
## Multiple R-squared: 0.617, Adjusted R-squared: 0.6123
## F-statistic: 131.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count 0.622220112673812
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0084 -0.7434 0.0378 0.9129 3.5820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97983 0.09043 110.357 < 2e-16 ***
## category_code_LT01_4_count 0.83328 0.08791 9.479 < 2e-16 ***
## category_code_LT01_5_count 0.92865 0.06198 14.983 < 2e-16 ***
## category_code_LT01_10_count 0.14464 0.11205 1.291 0.197
## category_code_LT01_11_count 0.46335 0.11424 4.056 5.81e-05 ***
## category_code_LT01_12_count -0.04523 0.21242 -0.213 0.831
## category_code_LT01_13_count 0.07215 0.24361 0.296 0.767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6222
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count 0.622156278316441
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0090 -0.7463 0.0354 0.9335 3.5810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98042 0.09091 109.787 < 2e-16 ***
## category_code_LT01_4_count 0.83497 0.08846 9.439 < 2e-16 ***
## category_code_LT01_5_count 0.92866 0.06225 14.918 < 2e-16 ***
## category_code_LT01_10_count 0.14386 0.11461 1.255 0.210
## category_code_LT01_11_count 0.46516 0.11410 4.077 5.32e-05 ***
## category_code_LT01_12_count -0.04538 0.21287 -0.213 0.831
## category_code_LT01_14_count 0.02314 0.33578 0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6222
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count 0.622154522797174
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0088 -0.7489 0.0349 0.9338 3.5805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97988 0.09046 110.326 < 2e-16 ***
## category_code_LT01_4_count 0.83548 0.08784 9.511 < 2e-16 ***
## category_code_LT01_5_count 0.92911 0.06198 14.991 < 2e-16 ***
## category_code_LT01_10_count 0.14508 0.11238 1.291 0.197
## category_code_LT01_11_count 0.46472 0.11436 4.064 5.62e-05 ***
## category_code_LT01_12_count -0.04404 0.21255 -0.207 0.836
## category_code_LT01_15_count 0.03738 0.75270 0.050 0.960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6222
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_16_count 0.622712357924092
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0086 -0.7394 0.0372 0.9120 3.5866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98075 0.09038 110.431 < 2e-16 ***
## category_code_LT01_4_count 0.83422 0.08744 9.541 < 2e-16 ***
## category_code_LT01_5_count 0.92756 0.06195 14.973 < 2e-16 ***
## category_code_LT01_10_count 0.14092 0.11206 1.257 0.209
## category_code_LT01_11_count 0.46127 0.11410 4.043 6.14e-05 ***
## category_code_LT01_12_count -0.04230 0.21227 -0.199 0.842
## category_code_LT01_16_count 0.99304 1.16352 0.853 0.394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6227
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count 0.622187791658881
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0077 -0.7412 0.0437 0.9126 3.5887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98015 0.09089 109.805 < 2e-16 ***
## category_code_LT01_4_count 0.83216 0.08890 9.361 < 2e-16 ***
## category_code_LT01_5_count 0.92714 0.06206 14.940 < 2e-16 ***
## category_code_LT01_10_count 0.14243 0.11463 1.242 0.215
## category_code_LT01_11_count 0.45671 0.11003 4.151 3.9e-05 ***
## category_code_LT01_13_count 0.07160 0.24361 0.294 0.769
## category_code_LT01_14_count 0.01932 0.33505 0.058 0.954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6222
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count 0.622189823574808
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7383 0.0434 0.9130 3.5886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97977 0.09045 110.335 < 2e-16 ***
## category_code_LT01_4_count 0.83227 0.08831 9.424 < 2e-16 ***
## category_code_LT01_5_count 0.92758 0.06175 15.021 < 2e-16 ***
## category_code_LT01_10_count 0.14313 0.11236 1.274 0.203
## category_code_LT01_11_count 0.45624 0.11025 4.138 4.12e-05 ***
## category_code_LT01_13_count 0.07277 0.24417 0.298 0.766
## category_code_LT01_15_count 0.05824 0.75398 0.077 0.938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6268, Adjusted R-squared: 0.6222
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_16_count 0.622763301399159
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0073 -0.7296 0.0472 0.8943 3.5945
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98061 0.09037 110.443 < 2e-16 ***
## category_code_LT01_4_count 0.83098 0.08786 9.458 < 2e-16 ***
## category_code_LT01_5_count 0.92598 0.06172 15.003 < 2e-16 ***
## category_code_LT01_10_count 0.13908 0.11204 1.241 0.215
## category_code_LT01_11_count 0.45299 0.11002 4.117 4.5e-05 ***
## category_code_LT01_13_count 0.07931 0.24358 0.326 0.745
## category_code_LT01_16_count 1.00980 1.16416 0.867 0.386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6273, Adjusted R-squared: 0.6228
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count 0.622123844773361
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0081 -0.7399 0.0375 0.9341 3.5871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98020 0.09092 109.772 < 2e-16 ***
## category_code_LT01_4_count 0.83432 0.08882 9.393 < 2e-16 ***
## category_code_LT01_5_count 0.92764 0.06206 14.949 < 2e-16 ***
## category_code_LT01_10_count 0.14287 0.11496 1.243 0.215
## category_code_LT01_11_count 0.45819 0.11006 4.163 3.71e-05 ***
## category_code_LT01_14_count 0.01859 0.33508 0.055 0.956
## category_code_LT01_15_count 0.04315 0.75229 0.057 0.954
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.384 on 491 degrees of freedom
## Multiple R-squared: 0.6267, Adjusted R-squared: 0.6221
## F-statistic: 137.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_16_count 0.622690706070593
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0081 -0.7310 0.0450 0.9112 3.5940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98154 0.09084 109.875 < 2e-16 ***
## category_code_LT01_4_count 0.83243 0.08844 9.413 < 2e-16 ***
## category_code_LT01_5_count 0.92579 0.06203 14.924 < 2e-16 ***
## category_code_LT01_10_count 0.13749 0.11471 1.199 0.231
## category_code_LT01_11_count 0.45493 0.10985 4.141 4.06e-05 ***
## category_code_LT01_14_count 0.03602 0.33544 0.107 0.915
## category_code_LT01_16_count 1.00337 1.16563 0.861 0.390
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6272, Adjusted R-squared: 0.6227
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_11_count+category_code_LT01_15_count+category_code_LT01_16_count 0.622687508993818
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0078 -0.7327 0.0436 0.9120 3.5930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98072 0.09040 110.409 < 2e-16 ***
## category_code_LT01_4_count 0.83318 0.08778 9.491 < 2e-16 ***
## category_code_LT01_5_count 0.92656 0.06172 15.013 < 2e-16 ***
## category_code_LT01_10_count 0.13937 0.11237 1.240 0.215
## category_code_LT01_11_count 0.45452 0.11005 4.130 4.26e-05 ***
## category_code_LT01_15_count 0.06457 0.75213 0.086 0.932
## category_code_LT01_16_count 0.99911 1.16414 0.858 0.391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 491 degrees of freedom
## Multiple R-squared: 0.6272, Adjusted R-squared: 0.6227
## F-statistic: 137.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.609565027419017
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0064 -0.7730 0.0241 0.9198 3.8828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97298 0.09239 107.944 <2e-16 ***
## category_code_LT01_4_count 1.05559 0.07097 14.874 <2e-16 ***
## category_code_LT01_5_count 0.93560 0.06327 14.788 <2e-16 ***
## category_code_LT01_10_count 0.16937 0.11636 1.455 0.146
## category_code_LT01_12_count 0.18630 0.20842 0.894 0.372
## category_code_LT01_13_count 0.12369 0.24733 0.500 0.617
## category_code_LT01_14_count 0.01692 0.34134 0.050 0.960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.60966306599522
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0070 -0.7731 0.0262 0.9195 3.8851
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97322 0.09193 108.492 <2e-16 ***
## category_code_LT01_4_count 1.05174 0.07088 14.838 <2e-16 ***
## category_code_LT01_5_count 0.93615 0.06298 14.865 <2e-16 ***
## category_code_LT01_10_count 0.16708 0.11414 1.464 0.144
## category_code_LT01_12_count 0.18813 0.20795 0.905 0.366
## category_code_LT01_13_count 0.12922 0.24780 0.521 0.602
## category_code_LT01_15_count 0.27126 0.76485 0.355 0.723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6144, Adjusted R-squared: 0.6097
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.610381718464307
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0060 -0.7739 0.0235 0.9197 3.8873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97376 0.09183 108.613 <2e-16 ***
## category_code_LT01_4_count 1.05157 0.06982 15.061 <2e-16 ***
## category_code_LT01_5_count 0.93395 0.06295 14.837 <2e-16 ***
## category_code_LT01_10_count 0.16462 0.11376 1.447 0.149
## category_code_LT01_12_count 0.18700 0.20773 0.900 0.368
## category_code_LT01_13_count 0.13231 0.24721 0.535 0.593
## category_code_LT01_16_count 1.20074 1.18217 1.016 0.310
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6151, Adjusted R-squared: 0.6104
## F-statistic: 130.8 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609448590929432
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0076 -0.7740 0.0204 0.9336 3.8838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97346 0.09243 107.908 <2e-16 ***
## category_code_LT01_4_count 1.05772 0.07098 14.901 <2e-16 ***
## category_code_LT01_5_count 0.93663 0.06326 14.805 <2e-16 ***
## category_code_LT01_10_count 0.16805 0.11673 1.440 0.151
## category_code_LT01_12_count 0.19041 0.20839 0.914 0.361
## category_code_LT01_14_count 0.01581 0.34138 0.046 0.963
## category_code_LT01_15_count 0.24580 0.76349 0.322 0.748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6142, Adjusted R-squared: 0.6094
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.610163189802738
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0070 -0.7746 0.0001 0.9341 3.8872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97459 0.09234 108.025 <2e-16 ***
## category_code_LT01_4_count 1.05658 0.07008 15.077 <2e-16 ***
## category_code_LT01_5_count 0.93414 0.06324 14.772 <2e-16 ***
## category_code_LT01_10_count 0.16393 0.11644 1.408 0.160
## category_code_LT01_12_count 0.18866 0.20817 0.906 0.365
## category_code_LT01_14_count 0.03589 0.34168 0.105 0.916
## category_code_LT01_16_count 1.18624 1.18395 1.002 0.317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.6149, Adjusted R-squared: 0.6102
## F-statistic: 130.6 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.610253576738659
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0073 -0.7747 0.0148 0.9385 3.8884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97431 0.09186 108.576 <2e-16 ***
## category_code_LT01_4_count 1.05372 0.06984 15.087 <2e-16 ***
## category_code_LT01_5_count 0.93505 0.06294 14.856 <2e-16 ***
## category_code_LT01_10_count 0.16308 0.11414 1.429 0.154
## category_code_LT01_12_count 0.19138 0.20772 0.921 0.357
## category_code_LT01_15_count 0.26968 0.76306 0.353 0.724
## category_code_LT01_16_count 1.19181 1.18223 1.008 0.314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.406 on 491 degrees of freedom
## Multiple R-squared: 0.615, Adjusted R-squared: 0.6103
## F-statistic: 130.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.609022363070979
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0119 -0.7673 0.0333 0.9226 3.8808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97472 0.09247 107.869 <2e-16 ***
## category_code_LT01_4_count 1.06603 0.07021 15.184 <2e-16 ***
## category_code_LT01_5_count 0.94107 0.06305 14.927 <2e-16 ***
## category_code_LT01_10_count 0.16964 0.11680 1.452 0.147
## category_code_LT01_13_count 0.13550 0.24792 0.547 0.585
## category_code_LT01_14_count 0.03811 0.34082 0.112 0.911
## category_code_LT01_15_count 0.26122 0.76541 0.341 0.733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.609
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.609762013329837
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0112 -0.7654 0.0303 0.9222 3.8843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97584 0.09238 107.991 <2e-16 ***
## category_code_LT01_4_count 1.06471 0.06923 15.379 <2e-16 ***
## category_code_LT01_5_count 0.93844 0.06302 14.890 <2e-16 ***
## category_code_LT01_10_count 0.16547 0.11650 1.420 0.156
## category_code_LT01_13_count 0.13897 0.24732 0.562 0.574
## category_code_LT01_14_count 0.05846 0.34112 0.171 0.864
## category_code_LT01_16_count 1.21302 1.18530 1.023 0.307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6145, Adjusted R-squared: 0.6098
## F-statistic: 130.4 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609850523923121
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0113 -0.7647 0.0305 0.9300 3.8844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97500 0.09191 108.528 <2e-16 ***
## category_code_LT01_4_count 1.06248 0.06883 15.435 <2e-16 ***
## category_code_LT01_5_count 0.93984 0.06268 14.994 <2e-16 ***
## category_code_LT01_10_count 0.16606 0.11413 1.455 0.146
## category_code_LT01_13_count 0.14466 0.24782 0.584 0.560
## category_code_LT01_15_count 0.28702 0.76501 0.375 0.708
## category_code_LT01_16_count 1.21563 1.18365 1.027 0.305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6146, Adjusted R-squared: 0.6099
## F-statistic: 130.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_10_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.609602316252611
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0126 -0.7717 0.0280 0.9215 3.8850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.97635 0.09242 107.947 <2e-16 ***
## category_code_LT01_4_count 1.06778 0.06911 15.450 <2e-16 ***
## category_code_LT01_5_count 0.93973 0.06302 14.912 <2e-16 ***
## category_code_LT01_10_count 0.16437 0.11687 1.406 0.160
## category_code_LT01_14_count 0.05747 0.34118 0.168 0.866
## category_code_LT01_15_count 0.25867 0.76361 0.339 0.735
## category_code_LT01_16_count 1.20221 1.18540 1.014 0.311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.407 on 491 degrees of freedom
## Multiple R-squared: 0.6143, Adjusted R-squared: 0.6096
## F-statistic: 130.3 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count 0.621028843926765
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0410 -0.7320 0.0331 0.9121 3.7019
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01313 0.08718 114.851 < 2e-16 ***
## category_code_LT01_4_count 0.84045 0.08880 9.465 < 2e-16 ***
## category_code_LT01_5_count 0.92765 0.06236 14.877 < 2e-16 ***
## category_code_LT01_11_count 0.47150 0.11424 4.127 4.31e-05 ***
## category_code_LT01_12_count -0.04125 0.21317 -0.194 0.847
## category_code_LT01_13_count 0.08098 0.24391 0.332 0.740
## category_code_LT01_14_count 0.11273 0.32867 0.343 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count 0.620962024446693
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0412 -0.7318 0.0313 0.9096 3.7041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01180 0.08711 114.932 < 2e-16 ***
## category_code_LT01_4_count 0.84393 0.08804 9.586 < 2e-16 ***
## category_code_LT01_5_count 0.92983 0.06208 14.978 < 2e-16 ***
## category_code_LT01_11_count 0.47023 0.11458 4.104 4.75e-05 ***
## category_code_LT01_12_count -0.03487 0.21275 -0.164 0.870
## category_code_LT01_13_count 0.08349 0.24445 0.342 0.733
## category_code_LT01_15_count 0.13271 0.75305 0.176 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_16_count 0.621599263280035
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0399 -0.7307 0.0334 0.9096 3.7064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01175 0.08704 115.028 < 2e-16 ***
## category_code_LT01_4_count 0.84305 0.08751 9.634 < 2e-16 ***
## category_code_LT01_5_count 0.92797 0.06205 14.955 < 2e-16 ***
## category_code_LT01_11_count 0.46709 0.11426 4.088 5.08e-05 ***
## category_code_LT01_12_count -0.03417 0.21248 -0.161 0.872
## category_code_LT01_13_count 0.08869 0.24387 0.364 0.716
## category_code_LT01_16_count 1.07874 1.16461 0.926 0.355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6216
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count 0.62096114020577
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0416 -0.7324 0.0315 0.9172 3.7013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01326 0.08719 114.844 < 2e-16 ***
## category_code_LT01_4_count 0.84219 0.08875 9.489 < 2e-16 ***
## category_code_LT01_5_count 0.92826 0.06235 14.887 < 2e-16 ***
## category_code_LT01_11_count 0.47229 0.11438 4.129 4.28e-05 ***
## category_code_LT01_12_count -0.03920 0.21329 -0.184 0.854
## category_code_LT01_14_count 0.11171 0.32872 0.340 0.734
## category_code_LT01_15_count 0.11273 0.75148 0.150 0.881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6255, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_16_count 0.621612658323325
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0402 -0.7315 0.0340 0.9196 3.7034
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01340 0.08711 114.945 < 2e-16 ***
## category_code_LT01_4_count 0.84055 0.08832 9.517 < 2e-16 ***
## category_code_LT01_5_count 0.92617 0.06233 14.860 < 2e-16 ***
## category_code_LT01_11_count 0.46900 0.11409 4.111 4.62e-05 ***
## category_code_LT01_12_count -0.03896 0.21299 -0.183 0.855
## category_code_LT01_14_count 0.12719 0.32880 0.387 0.699
## category_code_LT01_16_count 1.08558 1.16523 0.932 0.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6216
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_12_count+category_code_LT01_15_count+category_code_LT01_16_count 0.621522807119258
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0405 -0.7313 0.0324 0.9170 3.7057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01191 0.08704 115.020 < 2e-16 ***
## category_code_LT01_4_count 0.84477 0.08748 9.656 < 2e-16 ***
## category_code_LT01_5_count 0.92865 0.06205 14.967 < 2e-16 ***
## category_code_LT01_11_count 0.46785 0.11441 4.089 5.06e-05 ***
## category_code_LT01_12_count -0.03187 0.21259 -0.150 0.881
## category_code_LT01_15_count 0.13656 0.75121 0.182 0.856
## category_code_LT01_16_count 1.07004 1.16452 0.919 0.359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.386 on 491 degrees of freedom
## Multiple R-squared: 0.6261, Adjusted R-squared: 0.6215
## F-statistic: 137 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.621024476841858
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0400 -0.7311 0.0354 0.9167 3.7075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01269 0.08716 114.878 < 2e-16 ***
## category_code_LT01_4_count 0.83865 0.08926 9.395 < 2e-16 ***
## category_code_LT01_5_count 0.92685 0.06216 14.910 < 2e-16 ***
## category_code_LT01_11_count 0.46413 0.11024 4.210 3.03e-05 ***
## category_code_LT01_13_count 0.08319 0.24442 0.340 0.734
## category_code_LT01_14_count 0.10763 0.32785 0.328 0.743
## category_code_LT01_15_count 0.13421 0.75266 0.178 0.859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 491 degrees of freedom
## Multiple R-squared: 0.6256, Adjusted R-squared: 0.621
## F-statistic: 136.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.621688573329644
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0386 -0.7301 0.0370 0.9086 3.7096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01282 0.08708 114.980 < 2e-16 ***
## category_code_LT01_4_count 0.83701 0.08879 9.427 < 2e-16 ***
## category_code_LT01_5_count 0.92467 0.06213 14.882 < 2e-16 ***
## category_code_LT01_11_count 0.46087 0.10998 4.190 3.3e-05 ***
## category_code_LT01_13_count 0.08858 0.24383 0.363 0.717
## category_code_LT01_14_count 0.12348 0.32795 0.377 0.707
## category_code_LT01_16_count 1.10217 1.16584 0.945 0.345
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6263, Adjusted R-squared: 0.6217
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.621613828700473
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0390 -0.7301 0.0351 0.9082 3.7113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01143 0.08702 115.052 < 2e-16 ***
## category_code_LT01_4_count 0.84075 0.08798 9.556 < 2e-16 ***
## category_code_LT01_5_count 0.92728 0.06181 15.001 < 2e-16 ***
## category_code_LT01_11_count 0.46042 0.11023 4.177 3.5e-05 ***
## category_code_LT01_13_count 0.09164 0.24440 0.375 0.708
## category_code_LT01_15_count 0.15918 0.75242 0.212 0.833
## category_code_LT01_16_count 1.08845 1.16514 0.934 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6216
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_11_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.621612869653304
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0393 -0.7307 0.0385 0.9226 3.7087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01299 0.08709 114.973 < 2e-16 ***
## category_code_LT01_4_count 0.83878 0.08873 9.453 < 2e-16 ***
## category_code_LT01_5_count 0.92543 0.06213 14.895 < 2e-16 ***
## category_code_LT01_11_count 0.46196 0.11003 4.198 3.19e-05 ***
## category_code_LT01_14_count 0.12243 0.32799 0.373 0.709
## category_code_LT01_15_count 0.13788 0.75075 0.184 0.854
## category_code_LT01_16_count 1.09324 1.16571 0.938 0.349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 491 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6216
## F-statistic: 137.1 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count 0.608062769921894
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0448 -0.7860 -0.0034 0.9298 4.0304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01152 0.08866 112.918 <2e-16 ***
## category_code_LT01_4_count 1.06339 0.07172 14.826 <2e-16 ***
## category_code_LT01_5_count 0.93540 0.06339 14.756 <2e-16 ***
## category_code_LT01_12_count 0.19824 0.20870 0.950 0.343
## category_code_LT01_13_count 0.14235 0.24815 0.574 0.566
## category_code_LT01_14_count 0.12017 0.33426 0.360 0.719
## category_code_LT01_15_count 0.36492 0.76359 0.478 0.633
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.41 on 491 degrees of freedom
## Multiple R-squared: 0.6128, Adjusted R-squared: 0.6081
## F-statistic: 129.5 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_16_count 0.608859926289401
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0430 -0.7989 -0.0057 0.9305 4.0301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01175 0.08857 113.036 <2e-16 ***
## category_code_LT01_4_count 1.06320 0.07061 15.058 <2e-16 ***
## category_code_LT01_5_count 0.93255 0.06336 14.717 <2e-16 ***
## category_code_LT01_12_count 0.19561 0.20848 0.938 0.349
## category_code_LT01_13_count 0.14423 0.24757 0.583 0.560
## category_code_LT01_14_count 0.14009 0.33429 0.419 0.675
## category_code_LT01_16_count 1.31318 1.18429 1.109 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6136, Adjusted R-squared: 0.6089
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_13_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608931034272856
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0433 -0.7979 0.0110 0.9379 4.0318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01005 0.08848 113.131 <2e-16 ***
## category_code_LT01_4_count 1.06267 0.07020 15.139 <2e-16 ***
## category_code_LT01_5_count 0.93543 0.06306 14.834 <2e-16 ***
## category_code_LT01_12_count 0.20343 0.20786 0.979 0.328
## category_code_LT01_13_count 0.15168 0.24803 0.612 0.541
## category_code_LT01_15_count 0.39273 0.76301 0.515 0.607
## category_code_LT01_16_count 1.30681 1.18329 1.104 0.270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 491 degrees of freedom
## Multiple R-squared: 0.6137, Adjusted R-squared: 0.6089
## F-statistic: 130 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_12_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608767592699928
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0441 -0.8004 -0.0116 0.9332 4.0299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01194 0.08858 113.026 <2e-16 ***
## category_code_LT01_4_count 1.06435 0.07070 15.054 <2e-16 ***
## category_code_LT01_5_count 0.93385 0.06335 14.740 <2e-16 ***
## category_code_LT01_12_count 0.20058 0.20843 0.962 0.336
## category_code_LT01_14_count 0.13732 0.33434 0.411 0.681
## category_code_LT01_15_count 0.36005 0.76172 0.473 0.637
## category_code_LT01_16_count 1.30484 1.18417 1.102 0.271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6135, Adjusted R-squared: 0.6088
## F-statistic: 129.9 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################
## ########################################
## category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count
## approval_real_price_sum_by_by_approval_type_LT01 ~ category_code_LT01_4_count+category_code_LT01_5_count+category_code_LT01_13_count+category_code_LT01_14_count+category_code_LT01_15_count+category_code_LT01_16_count 0.608357043372209
##
## Call:
## lm(formula = multiple.formula_str %>% as.formula, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0489 -0.8032 0.0091 0.9276 4.0283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.01361 0.08860 113.015 <2e-16 ***
## category_code_LT01_4_count 1.07220 0.06995 15.327 <2e-16 ***
## category_code_LT01_5_count 0.93836 0.06314 14.861 <2e-16 ***
## category_code_LT01_13_count 0.15896 0.24811 0.641 0.522
## category_code_LT01_14_count 0.16230 0.33355 0.487 0.627
## category_code_LT01_15_count 0.38084 0.76356 0.499 0.618
## category_code_LT01_16_count 1.33698 1.18550 1.128 0.260
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.409 on 491 degrees of freedom
## Multiple R-squared: 0.6131, Adjusted R-squared: 0.6084
## F-statistic: 129.7 on 6 and 491 DF, p-value: < 2.2e-16
##
## ########################################